good documentation practices in clinical research

• Quality Assurance

Requirements for Good Documentation Practice (GDP)

today = new Date() if ((today.getHours() >=9) && (today.getHours() today = new Date() if ((today.getHours() >=18) && (today.getHours() =0) && (today.getHours() .reverse { unicode-bidi: bidi-override; direction: rtl; display:none;} Ankur Choudhary is India's first professional pharmaceutical blogger, author and founder of pharmaguideline.com, a widely-read pharmaceutical blog since 2008. Sign-up for the free email updates for your daily dose of pharmaceutical tips. .moc.enilediugamrahp@ofni :liamE Need Help: Ask Question

12 comments: Post Yours! Read Comment Policy ▼ PLEASE NOTE: Comments shall be published after review. Spams/ Promotional links are not allowed and shall be deleted upon review. You can ask questions related to this post here.

What about the digitalization of SOPs and records following a ZERO PAPER policy? In case we generate records only with an IT system is it mandatory to keep the raw data if we keep a scanned copy?

Nice share...

Is there any GDP policy regarding the register, logbook etc. can we use spiral binding log books and registers for data recording?

Spiral binding logbook is not a good option and auditor will not allow its pages can be changed easily.

In case of hand written document like ROA, if some of the details are same like reagent preparation or chemical details. is it allowed to fill it in one document and the other document has its reference no. only?

If your using same chemical on the same day it is ok.

Dear Sir, We are using different logbooks but some have only "done by" option or only "checked by" option and some have both options. It's confusing. Is it required to have both "done by" and "checked by" option in Logbook?

Yes both are required.

Is there any reference for not to use spiral binding or any observation latter on it?

Anything less than four-eye principle is a violation of GMP.

Thank you sor

If I had multiple printouts (data), all associated with one particular test - each page automatically states Page 1 of 1 - is it possible to staple all the associated pages together (treated now as one document/attachment), sign the front page as the person it was performed by and pagerise the remaining pages?

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• Open access
• Published: 18 July 2024

Accuracy, thoroughness, and quality of outpatient primary care documentation in the U.S. Department of Veterans Affairs

• Michael Weiner 1 , 2 , 3 ,
• Mindy E. Flanagan 1 ,
• Katie Ernst 4 ,
• Ann H. Cottingham 3 ,
• Nicholas A. Rattray 1 , 2 , 3 ,
• Zamal Franks 1 ,
• April W. Savoy 1 , 3 , 5 ,
• Joy L. Lee 6 &
• Richard M. Frankel 1 , 2 , 3  

BMC Primary Care volume  25 , Article number:  262 ( 2024 ) Cite this article

38 Accesses

Metrics details

Electronic health records (EHRs) can accelerate documentation and may enhance details of notes, or complicate documentation and introduce errors. Comprehensive assessment of documentation quality requires comparing documentation to what transpires during the clinical encounter itself. We assessed outpatient primary care notes and corresponding recorded encounters to determine accuracy, thoroughness, and several additional key measures of documentation quality.

Patients and primary care clinicians across five midwestern primary care clinics of the US Department of Veterans Affairs were recruited into a prospective observational study. Clinical encounters were video-recorded and transcribed verbatim. Using the Physician Documentation Quality Instrument (PDQI-9) added to other measures, reviewers scored quality of the documentation by comparing transcripts to corresponding encounter notes. PDQI-9 items were scored from 1 to 5, with higher scores indicating higher quality.

Encounters ( N  = 49) among 11 clinicians were analyzed. Most issues that patients initiated in discussion were omitted from notes, and nearly half of notes referred to information or observations that could not be verified. Four notes lacked concluding assessments and plans; nine lacked information about when patients should return. Except for thoroughness, PDQI-9 items that were assessed achieved quality scores exceeding 4 of 5 points.

Conclusions

Among outpatient primary care electronic records examined, most issues that patients initiated in discussion were absent from notes, and nearly half of notes referred to information or observations absent from transcripts. EHRs may contribute to certain kinds of errors. Approaches to improving documentation should consider the roles of the EHR, patient, and clinician together.

Peer Review reports

The accuracy of clinical documentation has always been crucial for many aspects of healthcare service delivery [ 1 ]. Principles underlying good note-keeping include timeliness, completeness, usefulness, synthesis (e.g., interpretation of findings, and diagnosis), and attention to clinical plans. Clinical reasoning should be clear. The note should be readily understood by other clinicians. When documentation originates outside the encounter, its source should be identified. In the U.S., medical records determine what can be billed, and records may be referenced in a court of law. In 1995, Frankel and Beckman published on the accuracy of the medical history [ 2 ]. As noted therein, medical records are used to judge the quality of care delivered.

Electronic health records (EHRs) have transformed progress notes, from documents written de novo into conglomerates of checkboxes, templates, imported text, transcription from dictation, and manually generated entries. Such enhancements can speed certain aspects of work and may enhance details of notes, but may simultaneously foster errors that threaten documentation accuracy. As complexity of both EHRs and documentation requirements has grown, concern has also grown about the increasing demands that EHR time, as well as documentation policies, have placed on health professionals [ 3 , 4 , 5 , 6 ]. Documentation quality has implications for patient safety and reimbursement, but a comprehensive assessment of documentation quality—which includes an understanding of how events in the clinical encounter align with documentation in the medical record—requires observation or recording of the encounters, so as to know which aspects of the encounters are included in the notes, and which details in the notes are or are not verifiable in the encounters.

Few have pursued this level of detail in examining the quality of documentation in EHRs, but three reports are noteworthy. First, focusing on inpatient care, Kelly et al. reported a 19-item progress note assessment and plan evaluation tool [ 7 ], but this tool describes a note’s content based on the presence of key elements, rather than on accuracy. Second, Stetson et al. developed a nine-item Physician Documentation Quality Instrument (PDQI-9) [ 8 ]. It includes scores reflecting accuracy and thoroughness, but the authors’ own use of the instrument was limited by retrospective assessments of medical records themselves, rather than direct comparisons of documentation against the corresponding clinical encounters. Third, Weiner et al. compared documentation to encounters, by obtaining concealed audio recordings of 36 physicians interacting with one of eight trained, unannounced actors portraying one of four cases [ 9 ]. In 105 outpatient encounters, they noted 455 undocumented (incomplete or not thorough) and 181 falsely documented (inaccurate) findings. Nonetheless, they did not assess other potentially important dimensions of notes, such as presence of key elements, usefulness, report of disease status, or follow-up plans. Since these attributes may be important additional areas of focus for improving outpatient documentation, we conducted a study of clinic encounters with real patients to assess electronic documentation quality that included measures of accuracy, thoroughness, and other key elements.

Study design

In this prospective observational study, the quality of primary care clinicians’ electronic documentation was investigated. Notes were scored for accuracy, thoroughness, and other quality indicators, using the PDQI-9 augmented with additional measures [ 8 , 10 ].

Setting and participants

Data collection occurred in the US at four primary care clinics at a midwestern Veterans Affairs (VA) Medical Center and one associated VA community-based outpatient clinic. For many years, the VA has used its own, homegrown comprehensive EHR system, including fully computerized progress notes for clinical encounters. The VA’s system uses a single, free-form, narrative text block for its progress notes, but has optional templates that can be used with such notes, with or without customization of a template by a user of the EHR system. A template, when selected, inserts into the note its generic text (e.g., for physical examination findings) or patient-specific lists (i.e., as actually recorded in the medical record) of previously documented medical problems, current medications, allergies, vital signs, or recent laboratory test results. In this manner, a template can be used to populate an entire note, or it can be used to add to a note that is in the process of being created. Our site did not use any scribes at the time of this study; team-based writing is also not the norm in our primary care setting. Clinicians may complete progress notes during their visits or within 24 h. Ethics approval was obtained from the Indiana University Institutional Review Board prior to study recruitment.

Provider recruitment and participation

Both primary care providers and their patients were included. A convenience sample of 12 primary care clinicians (physicians, advanced practice nurse practitioners, or advance practice nurses) was targeted. The clinicians were approached before the start of a clinic session to obtain informed consent and collect demographic information: age, gender, years since medical or nursing school graduation, and number of years working in the institution.

Patient recruitment and participation

For each primary care clinician enrolled, 50 adult patient participants were targeted. English-speaking patients 18 or more years of age who had been seen in the clinic at least once before the index visit were eligible. Patients with cognitive impairment according to their provider could participate if a caregiver was present. Patients who declined participation, or who were excluded by their provider, were replaced by selecting an alternate patient. A research assistant worked with providers to verify appropriateness of study participants. Informed consent was obtained.

For each participating provider, up to five video-recorded encounters could be included in the study. Only encounters in which both the patient and clinician consented were recorded. At the discretion of the participating patient, patient-support personnel such as family, friends, or informal caregivers could be present during the encounter.

Documentation quality measure

The PDQI-9 assesses the quality of electronic documentation on nine attributes: up-to-date, accurate, thorough, useful, organized, comprehensible, succinct, synthesized, and internally consistent. For each attribute, using a description of an ideal note, raters assigned a score from 1 (“Not at all”) to 5 (“Extremely”). For example, an accurate note is defined as, “The note is true. It is free of incorrect information”; a thorough note is defined as, “The note is complete and documents all [italics added] of the issues of importance to the patient.” Two attributes were excluded from our scoring procedure: first, due to the limits of our data collection that excluded other recent and pertinent notes, we did not score notes on “up-to-date”. Second, due to ambiguity in operationalizing “the note is extremely relevant” for the “useful” attribute, this attribute was excluded. The PDQI-9 has been shown to be valid and have high reliability [ 8 ]. Beyond the PDQI-9 elements themselves, the research team identified and assessed 18 additional elements thought to be important attributes or content of all notes in outpatient primary care. Presence of reason for the visit, summary of past medical history, medication list, vital signs, psychosocial concerns, assessment and plan, diagnoses, and status or severity of disease were assessed. These attributes added granularity to the PDQI-9 elements. Issues such as past medical history, medications, and diagnostic test results can be expected to be summarized in nearly all outpatient primary-care notes. In assessing accuracy and thoroughness, clinical issues were categorized as having been initiated by the patient or by the clinician. For thoroughness, issues were also categorized as biomedical or psychosocial. For example, if a patient was first to refer to food insecurity, this would be considered a patient-initiated psychosocial concern. Using six months of data prior to the interview date, timeliness of documentation was assessed by provider, by computing the percentage of notes generated during that period and completed within 24 h of the encounter. For individual notes assessed during the study period, we did not measure time to complete the note, because we could not determine how long a clinician worked on a note, and could not verify that a midstream equipment shutdown did not occur. Notes were retrieved following the 24-hour grace period for creating notes. The data collection form is provided in the Appendix .

Video recording

For most observations, the research assistant activated the recording equipment and then left the room during the encounter; in some cases, research personnel stayed in the room to position and operate the video recording equipment. Audio recordings were used to create verbatim de-identified transcripts of the encounters.

Data analysis

Five researchers with experience in psychology, sociology, human factors, anthropology, public health, or general internal medicine were assigned transcripts to review. Reviewers then reviewed assigned transcripts to ascertain symptoms, topics, and decisions included in the clinical encounters. All reviewers coded two encounters and discussed findings. The coding process was revised accordingly. Coding then began in pairs. The clinical note was reviewed and scored for the included PDQI-9 attributes and presence of the 18 additional elements. Accuracy and thoroughness concerns were marked against one attribute or the other, but not both. Each transcript-note set was reviewed independently by two researchers. Results were then compared in a series of discussions. All ratings were entered into Research Electronic Data Capture (REDCap), a secure, Web-based software platform [ 11 ]. Ratings for each attribute were averaged between the two raters. Ratings for the 18 elements were compared to identify disagreements. Where disagreements were identified, a third researcher reviewed the transcript-note set and served as a tie breaker. Summary statistics were calculated for attributes and elements.

Ten physicians and two nurse practitioners were recruited. One nurse practitioner was excluded due to incomplete data, so 11 providers’ data were analyzed. Recordings were collected for 49 clinical encounters. Characteristics of participants are shown in Table  1 . Six providers were women. 18% of patients were African American.

Key findings are shown in Table  2 . Twenty-five notes included a reason for the visit; 37 summarized the past medical history, and 32 included a medication list. Diagnostic test results were noted in 36. Assessment and plan were included in 45. Action plans were provided for all noted issues in 26 notes. For patient-initiated issues, 31 notes accurately reflected what was in the transcript; 21 did for clinician-initiated issues. Notes were judged as lacking in thoroughness—omitting one or more aspects of the encounter—in most cases except psychosocial issues initiated by the clinician. Examples of information not in the right place were past medical history combined with history of the present illness, and laboratory test results not in the “lab studies” or “data” section.

Overall ratings of documentation domains are summarized in Table  3 . Thoroughness had the lowest mean (3.7); accuracy was second to worst (4.0). Internal consistency and comprehensibility had the best scores. Provider-based means were similar to means across all observations.

The Fig.  1 shows documentation quality according to a provider’s timeliness of documentation during a six-month period. No definitive pattern emerged upon inspection.

Documentation quality according to providers’ timeliness of documentation, by provider ( N  = 11). Timeliness reflects six months of data prior to the interview date

Across 49 outpatient primary care encounters examined by five reviewers, all of the assessed PDQI-9 scores, except thoroughness, exceeded 4.0, indicating good quality as well as room for improvement. Fewer than half of the issues that patients initiated were included in notes. Reasons for such omissions could include clinicians’ lack of recognition of the problem, forgetfulness while writing notes, a belief that the issue had already been addressed, or a preference to prioritize other clinical issues. Team-based writing has been described as a potential solution [ 12 ]; some have asserted that medical scribes can decrease risks to patient safety by documenting at the point of care and relieving the clinician of the burden of doing so during or after the encounter [ 13 ]. Even when clinicians themselves identify clinical issues to be addressed, delays in writing notes—often manifest as EHR use after clinic hours (though not limited to note-writing) [ 14 ]— may increase the chance of omissions. Along with total EHR time, this “outside” EHR time has increased in recent years [ 15 ].

Minimizing the time from encounter to note may help in optimizing documentation quality. Regardless of the reason for suboptimal thoroughness, the findings reinforce the potential value of techniques and verbal summaries that explicitly communicate shared understanding of key issues and their action plans before the encounter ends. The Agency for Healthcare Research and Quality developed and disseminated a “teach-back” technique for health care professionals to communicate medical information clearly and accurately to patients and families [ 16 ]. We suggest adding a reciprocal role for clinicians to undergo teach-back and to identify and declare what the patient communicated to them . Such approaches are technically simple and would quickly enable patients to identify overlooked issues that warrant attention. Including this activity would need to be weighed against other priorities; one study of U.S. office visits, for example, revealed that a median of six topics were discussed in a median visit duration of 15.6 min [ 17 ].

Perhaps of greater concern than the findings pertaining to thoroughness is the imperfect accuracy observed, with close to half of notes referring to information or observations that could not be verified by reviewing the encounter transcript. This was not strictly historical information but information that was expected to be found in transcripts themselves. A likely contributor to this problem is the use of templates [ 18 , 19 ], which have become a norm in EHR systems. Some researchers have demonstrated different impacts of templates on note quality, depending on whether primary care clinicians or specialists were using them [ 20 ], or according to the specific measure being documented [ 21 ]. An additional method of speeding documentation (though without informational prompts), dictation, has been found to be comparable to, or worse than, other methods [ 21 ]. Some uses of templates improve thoroughness, probably via avoiding excessive reliance on human memory [ 22 ]. Templates can also save time by populating a note with pre-filled text, so that it does not need to be typed manually. Schnipper et al. developed a Smart Forms system to enable writing notes while capturing coded information and providing clinical decision support at the same time [ 23 ]. Although the uptake of the item form was low, it improved how issues were addressed during or after visits. Nonetheless, if all of the information represented in a template is not verified, the template can quickly result in any number of falsehoods. This may have negative consequences for billing integrity as well as for safety and effectiveness of medical care. Excessive reliance on templates might introduce biases that cause clinicians to avoid documenting specific types of issues not represented in the templates that are being used.

Psychosocial issues are common in primary care. In this study, when the clinician initiated discussion about such issues, 92% of notes included it, but when the patient initiated discussion, only 45% of notes did. This discrepancy suggests that primary care clinicians might not adequately recognize many psychosocial issues or assign enough importance to them, especially in situations where patients may provide only hints about their symptoms. Alternative explanations are that clinicians may feel unequipped to address some clinical issues related to psychosocial matters, or they may feel that other health professionals are responsible for addressing them, which could serve as a future research topic.

Structured documentation systems may lack sufficient flexibility and expressivity to address psychosocial concerns [ 24 ]. An innovative approach that could complement a teach-back strategy could be a computerized listener (audio processor) or ambient dictation technology that analyzes live conversation for “hidden” or hard-to-find issues in real time. From a more traditional perspective, the role of additional training to help clinicians identify psychosocial distress could be explored. Fanucchi and Conigliaro found that a lecture and individual feedback about progress notes did not lead to improvements in documentation quality [ 25 ]. By contrast, Habtamu and colleagues found that use of simulation and role play improved primary care clinicians’ detection of depression [ 26 ]. Other organizational, structural, or technical enhancements may be needed in designing interventions to improve note accuracy and completeness [ 26 ].

Other quality gaps raise additional questions. For example, with 8% of notes lacking an assessment and plan, were some assessments truly incomplete, and some important plans actually skipped? With 18% of notes missing follow-up plans, were some follow-up plans never arranged? With 26% lacking reports of diagnostic test results, were such results simply absent or unimportant, or were important findings unavailable, difficult to access, or overlooked? We recognize that certain variations in EHR documentation stem from authors’ preferences or styles about how to organize or structure notes [ 27 ]. At the same time, notes should not lack critical elements. Although this study did not aim to dive into additional details of these issues, further study is warranted. A longitudinal design in which recorded visits and notes could be compared with care delivery and outcomes would help answer these questions.

The study has several limitations. The sample size is small and not necessarily representative of a larger group of clinicians, whether in our institution or elsewhere. Due to the sample size, we also did not assess statistical significance of differences. The study was conducted in the VA health system, which may not be representative of other care systems. The cross-sectional design of the study did not permit us to assess the effect of continuity relationships between clinicians and patients. Some of the absences that were noted in the records of care may have been recalled by the clinician or the patient in subsequent visits. Finally, we do not have access to the clinicians’ lived experience of taking notes and whether they were aware of the differences between what was said and what was documented in the medical record. A future study would benefit from the use of cognitive task analysis or critical incident interviews with clinicians [ 28 , 29 ].

In summary, among outpatient primary care notes examined, fewer than half of issues that patients initiated in discussion were included in notes, and nearly half of notes referred to information or observations that could not be verified. Although EHRs have matured in certain ways, they may also contribute to a range of errors from minor to egregious. Improvements to documentation should consider the roles of the EHR, patient, and clinician together. Increasingly, documentation itself should become an active and interventional tool to improve care, instead of a passive means to archive an encounter.

Data availability

Our data are not publicly available due to the sensitivity and confidentiality of the detailed clinical notes and records that we examined.

Abbreviations

• Electronic health records

Physician Documentation Quality Instrument

Veterans Affairs

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Acknowledgements

We would like to acknowledge Melissa R. Thomas and Steve Becraft for their careful review of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs or the United States government.

Source of funding: U.S. Department of Veterans Affairs (HX002519).

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Center for Health Information and Communication, Department of Veterans Affairs, Health Services Research and Development Service, Veterans Health Administration, Richard L. Roudebush VA Medical Center, CIN 13 416, Indianapolis, IN, USA

Michael Weiner, Mindy E. Flanagan, Nicholas A. Rattray, Zamal Franks, April W. Savoy & Richard M. Frankel

Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

Michael Weiner, Nicholas A. Rattray & Richard M. Frankel

Indiana University Center for Health Services and Outcomes Research, Regenstrief Institute, Inc, 1101 West 10th Street, 46202-4800, Indianapolis, IN, USA

Michael Weiner, Ann H. Cottingham, Nicholas A. Rattray, April W. Savoy & Richard M. Frankel

Applied Decision Science LLC, Cincinnati, OH, USA

Katie Ernst

Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

April W. Savoy

Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA

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Contributions

RF and NR recruited providers in the study. MW, AC, MF, KE, and RF each reviewed and coded the transcripts. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Michael Weiner .

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Informed consent was obtained from all participants. Ethics approval was obtained from the Indiana University Institutional Review Board. The study was conducted in accordance with the Declaration of Helsinki.

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Not applicable (IRB documents available upon request).

Competing interests

Michael Weiner reports stock holdings in Allscripts Healthcare Solutions, Apple, Centene, DXC Technology, General Electric, Hewlett Packard, International Business Machines, Kyndryl Holdings, Micro Focus International, Microsoft, Oracle Cerner, Perkinelmer, Qualcomm, Walgreens, Zimmer Biomet, Intel, Cellcom Israel, Exact Sciences, IRhythm Technologies, Senseonics, Teladoc Health, and Varex Imaging. Remaining authors have no competing interest to declare.

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Health professionals’ routine practice documentation and its associated factors in a resource-limited setting: a cross-sectional study

Addisalem workie demsash.

1 College of Health Science, Health Informatics Department, Mettu University, Mettu, Ethiopia

Sisay Yitayih Kassie

Abiy tasew dubale, alex ayenew chereka, habtamu setegn ngusie.

2 College of Health Science, Health Informatics Department, Woldia University, Woldia, Ethiopia

Mekonnen Kenate Hunde

3 College of Education and Behavioral Science, Lifelong Learning and Community Development Department, Mettu University, Mettu, Ethiopia

Milkias Dugassa Emanu

4 College of Health Science, Nursing Department, Mettu University, Mettu, Ethiopia

Adamu Ambachew Shibabaw

Agmasie damtew walle, associated data.

All the data generated and analysed during this study are included, in the table and text form, in this article. If required, the data will be available on request from the corresponding author. Not applicable.

Documenting routine practice is significant for better diagnosis, treatment, continuity of care and medicolegal issues. However, health professionals’ routine practice documentation is poorly practised. Therefore, this study aimed to assess health professionals’ routine practice documentation and associated factors in a resource-limited setting.

An institution-based cross-sectional study design was used from 24 March up to 19 April 2022. Stratified random sampling and a pretested self-administered questionnaire were used among 423 samples. Epi Info V.7.1 and STATA V.15 software were used for data entry and analysis, respectively. Descriptive statistics and a logistic regression model were employed to describe the study subjects and to measure the strength of association between dependent and independent variables, respectively. A variable with a p value of <0.2 in bivariate logistic regression was considered for multivariable logistic regression. In multivariable logistic regression, ORs with 95% CIs and a p value of <0.05 were considered to determine the strength of association between dependent and independent variables.

Health professionals’ documentation practice was 51.1% (95% CI: 48.64 to 53.1). Lack of motivation (adjusted OR (AOR): 0.41, 95% CI: 0.22 to 0.76), good knowledge (AOR: 1.35, 95% CI: 0.72 to 2.97), taking training (AOR: 4.18, 95% CI: 2.99 to 8.28), using electronic systems (AOR: 2.19, 95% CI: 1.36 to 3.28), availability of standard documentation tools (AOR: 2.45, 95% CI: 1.35 to 4.43) were statistically associated factors.

Conclusions

Health professionals’ documentation practice is good. Lack of motivation, good knowledge, taking training, using electronic systems and the availability of documentation tools were significant factors. Stakeholders should provide additional training, and encourage professionals to use an electronic system for documentation practices.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Although routine practice documentation is a legal obligation and crucial for the continuity of patient care, health professionals’ documentation practices are poor, contain errors that further affect patient outcomes and create distorted health information.

WHAT THIS STUDY ADDS

• This study assesses routine practice documentation in resource-limited setting including all types of health professionals, and identify associated factors.
• Additionally, whether health professionals’ documentation of education and counselling they give to patients and use an electronic system was assessed.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• This study is crucial for health policy formulators, planners and implementers to enhance health professionals’ motivation for better documentation practice.
• This study may motivate health professionals to use an electronic system for documentation practice as much as possible.
• The evidence would serve as input for future similar studies.

Introduction

In routine healthcare practice, evidence about the care and treatment of patients, progress notes, assessments and care plans, 1 laboratory tests and results, medication and drug prescription information, patient education and counselling 2 are some of the routine practices of health professionals. Therefore, documenting the health professionals’ routine practices are important for various purposes.

Documentation is a standard way of keeping ongoing patient care information. It is the relevant facts of routine health information and patient care plans, 3 such as professionals’ evaluation and judgement about the patients, evaluation charts, tests, reports, subjective notes or professionals’ reflections. 4

Documenting routine practices is essential for the continuity of patient care, legal defence, reimbursement, communication among healthcare professionals and better patient diagnoses and treatments. 5 Maintaining routine practice is part of the health professional obligation. Healthcare facilities’ by-laws or policies should require health professionals to complete patient records. 6 Whether the documentation is a paper-based or electronic system, it should be patient-focused, accurate, relevant, clear, permanent, confidential and timely. Electronic patient record systems are better for reducing the time spent on documenting patient information and enhancing the quality of documentation. 7

Poor documentation practice affects patient management, continuity of patient care and medicolegal issues, which arise from incomplete and inadequate documentation, lack of accuracy and poor quality. 8 It leads to adverse patient outcomes, medication errors and patient deaths. 9 Distorted health information may influence health professionals’ decision-making capabilities due to inappropriate and misleading documentation practices. 10

Globally, poor communication between health professionals is a reason for medical error and patient mortality. 9 Many health professionals’ documentation practice is incomplete, inaccurate and of poor quality. According to evidence from the USA, documentation errors are a cause of at least one death and 1.3 million injuries annually. 11 Moreover, health professionals’ documentation practice is inadequate such as 33.3% in Indonesia, 12 47% in England 13 and 50% in Iran. 14

In the low-income and middle-income regions, a qualitative study undertaken in Uganda stated that documentation practice is limited by constraints and poor support from the administration. 15 In Ghana, 46% of care is provided, and progress notes are not documented after the first day of patient admission. 16 In Nigeria, only 44% of health professionals had good documentation knowledge and practice. 17

In Ethiopia, documentation is poorly practised and has been reported as being left undone. 3 Health professionals’ documentation practice is 47.8% in the Tigray 18 and 37.4% 3 in Amhara regions. Surprisingly, 88% of the medication provided has been wrongly documented. 19 A study report in the Amhara region states that 87% of the medications had documentation errors. 19

Age, sex, experience, income, levels of education, health professionals’ knowledge and attitude, 3 12 18 motivation, workload and training about documentation 20 are factors associated with routine practice documentation.

Documenting health professionals’ routine activities is valuable for sharing knowledge and learning from history. This has a significant impact on better decision-making and accuracy in patient diagnosis and treatment. As per our literature review, studies have not been undertaken in the current study setting. Few studies in similar settings have been carried out with only nursing as a study participants, education and counselling given to the patient were not assessed. So, assessment documentation practice in both medical and non-medical practices, including all health professionals is crucial. Therefore, this study aimed to determine health professionals’ routine practice documentation and associated factors.

Study design and period

An institutional-based cross-sectional study design was employed among health professionals working in public health facilities in the Ilu Aba bora Zone, from 24 March up to 19 April 2022.

Study setting

Ilu Aba Bora Zone is found in Southwest Ethiopia. The zone is located 600 km away from Addis Ababa, the capital city of Ethiopia. In the zone, there are 44 total health facilities and 2 hospitals (1 general hospital and 1 referral hospital). The public health facilities provide different health services for more than a million of the population in southwest parts of the country coming from Gambela, Southern Nation Nationality and People’s region.

Study population and eligibility criteria

All healthcare professionals working in the public health facilities of Ilu Aba Bora Zone and those who were found during the data collection period were the sources and study population, respectively. Healthcare professionals who were not permanently employed, those who were not present during the study period and who worked as data clerks were excluded.

Sampling size determination

A sample size (n) was determined by using a single population proportion formula, N=(Z a/2 ) 2 ×P(1−P)/d 2 , where n=the required sample size, (Za /2 ) 2 =the value of standard normal distribution or 1.96, p=the prevalence of documentation practice among health professionals and so the default maximum value of 50% was used for P, d=degree of precision or 0.05. Taking this, the required sample size was calculated to be 384. After adding a 10% non-response rate, a total of 423 healthcare professionals participated in this study.

Sampling producer

A stratified simple random sampling method was used. Due to the limited resources, we have to cover all types of health facilities, we have included two hospitals directly and three randomly selected health centres. Once the sample was stratified based on the types of randomly selected health facilities, the sample was allocated to each stratum proportionally. Then, a simple random sampling technique was used to select the study subjects in each public health facility. The list of health professionals was taken from human resource departments. Accordingly, there are 1043 health professionals from 5 randomly selected health facilities. The sampling procedure has been presented in figure 1 .

Sampling procedures of study participant selection. HC, health centres.

Operationalisations and measurements

In the healthcare system, patient status, medical diagnoses, planned care, medical interventions or treatments, laboratory tests, result confirmations, medications, patient education and counselling, communication and delivering service are activities of health professionals. All the mentioned activities of health professionals are either medical or non-medical activities (patient education and counselling), but all are routine activities for health professionals. As a result, health professionals use standard documentation tools such as manual records and/or electronic systems to document their routine activities correctly and on time while respecting the rules of ethics. 21 Accordingly, health professionals’ routine practice documentation was assessed by using 12 ‘yes’ and ‘no’ questions. 3 22 The level of health professionals’ routine practice documentation was determined using the mean value as a cut-off point. Hence, the level of health professionals’ routine practice documentation is good if the score is above or equal to the mean value, and otherwise poor documentation practice.

The study participants’ level of knowledge was measured by using 10 ‘yes’ and ‘no’ options. Health professionals who scored above or equal to the mean score were considered to have good knowledge, and those who scored below the mean value had poor knowledge. 3

The study participants’ level of attitude was measured by using nine Likert scale questions with responses ranging from 1 ‘strongly agree’ to 5 ‘strongly disagree’. 3 18 Health professionals who scored above or equal to the mean score were considered to have a good attitude, otherwise, poor attitude.

Data collection tool and quality assurance

The tool used was developed based on reviewing similar studies. 3 18 22 A pretested, self-administered questionnaire was used. Two supervisors and three data collectors received 2 days of intensive training on the study objectives and how to approach study participants. A pretest was done outside the study area with 10% of the study subjects to check the readability and consistency of the questionnaire. The data obtained from the pretest were used to check the validity and reliability of the tool. The Cronbach’s alpha was used to check the reliability of the tool with a value of 83.

Data processing and analysis

The data entry was performed using Epi Info V.7.1 software packages and analysed using STATA V.15 software. Descriptive statistics were computed to describe the sociodemographic characteristics of the healthcare professionals, their knowledge and their attitudes towards routine practice documentation. Bivariable and multivariable binary logistic regression analyses were conducted to measure the association between the dependent and independent variables. In the bivariable regression analysis, variables with a p value of <0.2 were considered for further multivariable logistic regression analysis. The OR with a 95% CI level was assumed to assess the strength of the association between dependent and predictor variables. For all significantly associated variables, a p value <0.05 was used as a cut-off point. A variance inflation factor was performed. Consequently, its value for all predictors was between one and three. This revealed that there was no correlation between the variables. The Hosmer-Lemeshow test was performed to assess the model fitness, and so model was fitted (p=0.271).

Description of study subjects

From 423 participants, 415 responded to a questionnaire with a 98.11% response rate. The mean age of the study subjects was 29.28 (SD±2.21) years with a minimum age of 21 years and a maximum age of 59 years. Half (51.1%) of the study subjects were male. The majority (66.7%) of study subjects were BSc degree holders or below. Of the total respondents, around 6–10 (62.4%) of the study participants had up to 5 years of working experience.

Less than half (32.3%) of the study subjects were trained in routine practice documentation. Of 415 study participants, 235 (56.6%) health professionals earned 4500–7500 Ethiopian Birr per month, and 8–10 (80.2%) health professionals responded that standard documentation tools were available in the working area. One hundred twenty-five (54.22%) health professionals used manual forms for documentation purposes ( table 1 ).

Sociodemographic characteristics of health professionals

Health professionals’ routine practice documentation

Overall, 51.1% (95% CI: 46.29% to 53.55%) of health professionals had good routine practice documentation; 6.99% of different laboratory test request forms were not completed and documented; 6.025% of the physicians’ prediagnosis was completed and documented; 5.54% of drug prescription and laboratory result forms were not completed and documented. Documentation incompleteness accounted for 32.52% of health professionals’ poor routine practice documentation ( table 2 ).

Checklists examine health professionals’ routine practice documentation adopted from the Ethiopian health institution reform implementation guidelines

Factors associated with routine practice documentation

Bivariate and multivariate logistic regressions were used to measure the association between dependent and independent predictors. In the bivariate logistic regression, p<0.2 was used and sex, age, training, knowledge, attitude, types of documentation tools, availability of standard documentation tools, workload and motivation of study subjects were the candidate variables for the multivariable regression analysis. In the multivariable regression model, knowledge, training, motivation, types and availability of the standard documentation tools were significant factors for routine practice documentation ( table 3 ).

Bivariate and multivariate analysis of factors associated with health professionals’ routine practice documentation (n=415)

Reference category=1.

*Significant in COR.

†Significant in AOR.

AOR, adjusted OR; COR, crude OR.

Health professionals who lack motivation were 59% (adjusted OR (AOR): 0.41, 95% CI: 0.22 to 0.76) less likely to document routine practices. Health professionals who had good knowledge of routine practice documentation were 1.4 (AOR: 1.35, 95% CI: 0.72 to 2.97) times more likely to document routine practice than those who had poor knowledge. Health professionals who were trained in routine practice documentation were 4.2 (AOR: 4.18, 95% CI: 2.99 to 8.28) times more likely to document routine practices than those who were not trained. Health professionals who used electronic systems for routine practice documentation were 2.2 (AOR: 2.19, 95% CI: 1.36 to 3.28) times more likely to document their routine practices than those who used manual forms for documentation. The availability of standard documentation tools were 2.5 (AOR: 2.45, 95% CI: 1.35 to 4.43) times more odds for health professionals to document their routine practices ( table 3 ).

This study assesses health professionals’ routine practice documentation and associated factors. Health professionals who had good knowledge about routine practice documentation, training on documentation, using electronic systems for documentation, the availability of standard documentation tools and a lack of motivation towards routine practice documentation were statistically significant factors associated with health professionals’ routine practice documentation.

The study revealed that health professionals’ routine practice documentation was good (51.1%). This finding is higher than previous similar studies, which found 44.2% in Nigeria, 17 33.3% in Indonesia 12 and 37.4%–48.8% in different parts of Ethiopia. 3 18 23 However, the finding is lower than the study done in Jamaica, which reports that health professionals’ documentation practice is 98%. 24 This might be due to the utilisation of technologies such as the electronic medical record and district health information system V.2 (DHIS2), the accessibility of required tools for documentation and health professionals’ good commitment to using DHIS2 data. 19 Additionally, this variation might be due to the information difference, the time gap between studies, the high patient flow, the shortage of time and the workload of health professionals.

Health professionals who lack motivation were 59% less likely to have documentation practices when compared with those who had gained motivation. This finding is inconsistence with studies done in Ethiopia. 23 25 This might be poor professional encouragement, poor financial support, less opportunities for further educational development, poor infrastructures and low hospital management support. 25

Health professionals for whom standard documentation tools were available were 2.5 times more likely to document routine practices than those for whom standard documentation tools were not available. This finding is consistence with a study done in Australia, 26 Tigray 18 and Amhara regions. 23 This might be due to familiarisation with standard documentation sheets, and the accessibility of integrated routine health information forms for recording and reporting. 27

Health professionals who had good knowledge of routine practice documentation were more likely to document their routine practice. This result is supported by studies done in Ethiopia, 3 the USA 28 and Australia. 29 This might be due to health professionals’ familiarity with documentation guidelines and manual forms that improve health professionals’ knowledge of routine practice documentation. 3 Additionally, the reason might be that health professionals understand the importance of documenting routine practice, the viability of reading materials, know that record-keeping is required for medicolegal issues and have good competency in the area of documentation. 29 Moreover, spending on documentation courses may promote health professionals’ knowledge. 30

Health professionals who were trained in routine practice documentation were 4.2 times more likely to document routine practices than those who were not trained. This evidence is supported by studies done in Ethiopia 3 and Iran. 30 This might be due to training, which might enhance health professionals’ knowledge and motivation for documentation and provide team-based learning, intrapersonal skills sharing and consultation gained from colleagues. Plus, training may force health professionals to develop a positive attitude towards routine practice documentation. 3

Health professionals who used electronic systems for routine practice documentation were 2.2 times more likely to document their routine practices than those who used manual forms. This study is supported by a study done in Ethiopia 20 and a study done about maintaining practices and record-keeping. 1 This might be due to the capability of electronic systems to reduce the time spent documenting patient care. 7 Additionally, record-keeping in the light of recent public inquiries, national interests in shifting from paper to digital storage of data, the capability of electronic health records to generate a complete record of an episode of care and the longitudinal nature of the electronic system might be possible reasons. 1 Moreover, a need for real-time access to health information when and where it is needed might be reason why an electronic system could be more likely to be good for documentation. 23

Strength and limitation

Since the data were collected at a specific time, social desirability bias may occur. Significant variables may have a temporal relationship. This study assesses the use of electronic systems for documentation as an independent variable. All health professionals were included, and documentation regarding education and counselling was assessed. Hence, the finding is unique as compared with previous studies. Moreover, the mean value was used as a cut-off point to determine level of health professionals’ routine practice documentation to consider the weighted average values.

In this study, health professionals have good routine practice documentation. Knowledge, training, using an electronic system, availability of standard documentation tools and lack of motivation are statistically significant factors for routine practice documentation. Health policy formulators and stakeholders give additional training to health professionals, and encourage them to use an electronic system for documentation. Stakeholders should improve health professionals’ knowledge and motivation of routine practice documentation. Additional high-quality studies are required on a similar topic.

Contributors: AWD had made a substantial contribution in writing the conception, designing the study, analyzing the data, and discussing the findings. SYK and ATD were involved in drafting the manuscript and interpreting the results. AAC and HSN had revised the manuscript. MKH, AAS, ADW, and MDE all made significant contributions to revising the manuscript, and managing the data. All authors read and approved the final manuscript submission for publication.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

Ethics approval was obtained from the Institutional Review Committee of the health science college of Mettu University with reference number ARcsu/271/24. Informed verbal and written consent from the study participant was obtained.

Clinical Researcher

Guidance for Preparing Standard Operating Procedures (SOPs)

Clinical Researcher October 17, 2023

Clinical Researcher—October 2023 (Volume 37, Issue 5)

CULTIVATING QUALITY

Olena Chervonenko, MS

Standard operating procedures (SOPs) play a vital role in ensuring quality, compliance, and safety across a pharmaceutical company’s operations. SOPs are key to ensuring users follow current requirements and best practices and perform tasks consistently and uniformly.

An SOP is a documented set of step-by-step instructions that outline the approved methods and practices for carrying out specific tasks or processes within an organization.

When writing SOPs, it’s important to strike the right balance between providing enough detail and avoiding excessive complexity. Insufficient detail increases the likelihood of users improvising on the spot or following widely used, but perhaps inadequate, methods. On the other hand, excessive information can lead to confusion, errors, and mistakes.

In the pharmaceutical industry, improvising or deviating from established methods can have serious consequences and may result in the company becoming noncompliant with one or more of the internationally agreed-upon “Good x Practices,” where “x” may stand for Clinical,{1} Manufacturing,{2} Laboratory,{3} etc.

SOPs must ensure the robustness, traceability, and integrity of a Pharmaceutical Quality System (PQS) of an organization and contribute to maintaining quality, safety, and compliance standards within the organization.

Robustness of the PQS

SOPs provide a systematic and documented approach to performing critical tasks and processes within an organization. By outlining specific procedures, controls, and requirements, SOPs help ensure the robustness of the PQS. They establish a framework that promotes consistency, traceability, and accountability in all operations.

SOPs incorporate safety considerations and guidelines to mitigate risks and protect personnel, patients, and the environment. They outline business workflows, safety protocols, personal protective equipment requirements, handling procedures for hazardous substances, and emergency response measures. By adhering to SOPs, organizations promote a culture of safety and minimize the potential for accidents or incidents.

In short, SOPs are essential tools in assuring the robustness of the PQS.

Traceability

Ensuring the traceability of the PQS is a critical aspect of SOPs within the pharmaceutical industry. Traceability provides a clear and documented path that demonstrates the implementation and effectiveness of the PQS. Here are some key considerations to incorporate traceability into your SOPs:

Documented References: SOPs should reference the relevant sections of the PQS documentation, such as quality manuals, policies, and guidelines. This ensures that SOPs are aligned with the overall quality system and current edition of guides.

Version Control: SOPs should have a version control system in place, indicating the specific version number, date of revision, and any relevant updates. This allows for easy identification of the most current version and facilitates traceability.

Cross-Referencing: SOPs should cross-reference other related SOPs or documents within the PQS. This ensures that interdependencies between procedures are clearly identified and maintained.

Change Control Process: The revision history of an SOP should clearly outline the specific changes that were made, accompanied by a linked change control document. This document should include thorough documentation of any modifications to the SOPs, along with justification and, if applicable, a risk assessment. This facilitates traceability and identification of the chronology of changes.

By incorporating these traceability elements into your SOPs, you establish a clear link between the procedures outlined in the SOPs and the overall PQS, ensuring transparency, compliance, and effective quality management.

Data Integrity

Data integrity is a critical aspect of SOPs within the pharmaceutical industry. It ensures that data generated, recorded, and reported during operations are accurate, complete, and reliable. SOPs must declare data integrity principles to prevent data manipulation, fraud, and errors. Key considerations for maintaining data integrity via SOPs include:

Data Recording: SOPs should outline the proper methods for recording data accurately, including clear instructions on data entry, identification, and verification.

Audit Trails: SOPs should define procedures for creating and maintaining audit trails, which provide a chronological record of data changes, ensuring data integrity and traceability.

Data Security: SOPs should incorporate measures to protect data integrity, such as access controls, data encryption, and regular data backups.

Bringing in the Right People

It is important to involve quality assurance (QA) and senior management in all stages of SOP creation — from ideation to assessing the effectiveness of implemented SOPs.

The QA department plays a crucial role in ensuring the effectiveness of SOPs within the pharmaceutical industry. QA staff are responsible for overseeing the development, implementation, and maintenance of SOPs to guarantee compliance with industry regulations and standards. Chiefly, they are responsible for SOP review, collaborating with subject matter experts to ensure SOPs are accurate, comprehensive, and aligned with regulatory requirements.

Senior management plays a key role in supporting and driving the effective implementation of SOPs. Their responsibilities include:

Policy and Strategy: Establishing clear policies and strategies that emphasize the importance of SOPs in ensuring quality, safety, and compliance throughout the organization.

Leadership and Communication: Promoting a culture of quality and compliance, fostering a shared understanding of the significance of SOPs at all levels of the organization. Senior management also communicates expectations, provides guidance, and encourages employee engagement.

Continuous Improvement: Supporting a continuous improvement mindset by encouraging feedback, monitoring of performance metrics, and implementing initiatives to enhance SOP effectiveness.

Focus of Inspection

During inspections, regulatory authorities and auditors focus on evaluating the implementation and effectiveness of SOPs. They assess whether the organization has well-documented and controlled SOPs, that these SOPs are followed, and that they are in compliance with regulatory requirements. Key areas of inspections related to SOPs include:

Process Description: Inspectors assess whether the organization has cohesively documented all GxP relevant activities.

SOP Availability: Inspectors verify that SOPs are readily available to personnel and accessible in relevant areas where tasks are performed.

Adherence to SOPs: Inspectors assess whether employees follow SOPs consistently, ensuring that documented procedures are being executed as specified.

Further Considerations

When creating or updating SOPs, it is important to keep in mind the following points to avoid unnecessary complexity:

Author and Reviewer: The author of the procedure should be someone who performs the task or possesses a deep understanding of it. They should strike a balance between providing adequate detail and avoiding excessive elaboration. The reviewer’s role is to ensure that the SOP achieves the appropriate level of detail.

Ask Why: Simplify and improve the workflow by questioning the necessity of each step in the SOP. This technique involves creating a comprehensive list of all the steps in a work process and critically evaluating their purpose.

Field Testing: Red line the procedure in the field to assess its relevance and coherence. This practice involves documenting and accurately recording the process while onsite, often accompanied by clear photos that can be included in the SOP.

Embrace Simplicity: Lengthy and intricate SOPs are challenging to follow. Strive to keep SOPs simple and user-friendly while ensuring that all important steps are included. Complex tasks can be broken down into smaller sub-steps. Sentences should generally be kept short to ensure clarity.

Visual Aids: Humans process visual information more easily than text. Improve the understandability of SOPs by incorporating photos, drawings, and flowcharts. Visual aids can significantly assist in conveying complex information.

Minimize Involvement: Involving fewer individuals in a task reduces the likelihood of human error. Streamlining the number of people involved in a task enhances efficiency and accuracy.

Audience Consideration: SOPs should be tailored to the education, experience, knowledge, and abilities of the personnel who will use them.

Language: SOPs should be written using vocabulary relevant to the area of use, experience, knowledge, and abilities of the personnel who will use them.

Testing and Verification: Before finalizing an SOP, conduct tests with individuals who will use it regularly. Their feedback can help identify unclear information, missing steps, or incorrect sequencing. It can also be beneficial to have someone with limited knowledge of the process test the SOP to ensure its accessibility to a broader audience.

Preserve Simplification Efforts: During the review process, carefully evaluate any additional or removed steps. Justification should be provided before incorporating any changes, being cautious to maintain the simplified nature of the SOP. This approach ensures that deviations are properly identified through root cause analysis.

Regular Review and Revision: Seek feedback from those who have direct experience using the SOP after several weeks of implementation. Evaluate clarity, effectiveness, readability, and ease of use. Revise the SOP based on the feedback received.

Keep SOPs Updated: Regularly review and update SOPs to align with current practices and requirements. Failure to periodically review and update SOPs can quickly render them obsolete. A recommended review frequency is every two to three years. The review frequency should be justified.

By following these guidelines and incorporating the necessary elements, SOPs can effectively guide pharmaceutical industry professionals, promote efficiency, support compliance and data integrity, and contribute to the overall success of an organization.

• Good clinical practice (GCP) E6(R3), ICH. https://database.ich.org/sites/default/files/ICH_E6%28R3%29_DraftGuideline_2023_0519.pdf
• Good manufacturing practice, EMA. https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice
• OECD Series on Principles of Good Laboratory Practice (GLP) and Compliance Monitoring, OECD. https://www.oecd.org/chemicalsafety/testing/oecdseriesonprinciplesofgoodlaboratorypracticeglpandcompliancemonitoring.htm

A list of SOPs required by the U.S. Food and Drug Administration, the European Union Guideline to Good Manufacturing Practice (GMP), and the World Health Organization for GMP activities can be found at  http://www.gmp-compliance.org/gmp-news/which-sops-are-required-by-gmp .

For Good Documentation Practice (GDP), the Health Products Regulatory Authority in Ireland has published a guidance document that includes the SOPs that are needed for compliance with GDP and details the general framework for each SOP at  https://www.hpra.ie/docs/default-source/publications-forms/guidance-documents/ia-g0038-guide-to-quality-system-for-general-sale-wholesale-distributors-v2.pdf?sfvrsn=14 .

European Medicines Agency Procedures, EMA: https://www.ema.europa.eu/en/procedures

Manual of Policies and Procedures, FDA: https://www.fda.gov/media/90280/download

The contents of this article are solely the opinion of the author and do not represent the opinions of PharmaLex GmbH or its parent Cencora Inc. PharmaLex and Cencora strongly encourage readers to review the references provided with this article and all available information related to the topics mentioned herein and to rely on their own experience and expertise in making decisions related thereto.

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• Arun J. Sanyal   ORCID: orcid.org/0000-0001-8682-5748 16 ,
• Naveed Sattar   ORCID: orcid.org/0000-0002-1604-2593 17 ,
• Marie-France Hivert   ORCID: orcid.org/0000-0001-7752-2585 18 ,
• Maria F. Gomez   ORCID: orcid.org/0000-0001-6210-3142 8 ,
• Jordi Merino   ORCID: orcid.org/0000-0001-8312-1438 12 , 19 , 20 , 21 ,
• Deirdre K. Tobias 2 , 22 ,
• Michael I. Trenell 23 ,
• Stephen S. Rich   ORCID: orcid.org/0000-0003-3872-7793 24 ,
• Jennifer L. Sargent 25 &
• Paul W. Franks   ORCID: orcid.org/0000-0002-0520-7604 2 , 26  

Nature Medicine volume  30 ,  pages 1874–1881 ( 2024 ) Cite this article

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Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.

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Precision medicine represents an evolution in the long history of evidence-based medicine and healthcare. Spanning disease classifications and risk factor boundaries, precision medicine is underpinned by four key ‘pillars’ (prevention, diagnosis, treatment and prognosis) 1 , 2 . The overarching objective of precision medicine is to reduce error and improve accuracy in medical and health recommendations compared with contemporary approaches 3 . Precision medicine solutions should meet or improve on existing standards for safety. They should also be compatible with the individual’s preferences, capabilities and needs and tailored to the cultural and societal conditions of the population. Furthermore, precision medicine should be cost-effective and enhance health equity by increasing access to better medical and healthcare practices for the people most in need.

Cardiometabolic diseases are the leading causes of mortality globally 4 . With this burden projected to worsen over the coming decades 5 , innovative approaches to disease prevention, diagnosis and treatment are urgently needed. A plethora of precision medicine approaches are being explored in translational and clinical research. However, translating, scaling and implementing these findings for clinical practice have proved difficult. The heterogeneous nature of disease presentation and the etiology of cardiometabolic diseases contribute to these challenges, as does the range and diversity of clinical information, molecular data types and computational analyses used in precision medicine research.

The ability to synthesize data and reproduce research findings are tenets of the modern scientific process, which help maximize progress in evidence-based healthcare and medicine. The ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 was supported by a series of systematic evidence reviews 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . The report focused on key dimensions of precision diabetes medicine, including evidence for prevention, diagnosis, treatment and prognosis in monogenic forms of diabetes, gestational diabetes, and type 1 and type 2 diabetes. A key finding from the report and the systematic evidence reviews underpinning it is that the published literature on precision diabetes medicine lacks evidence standardization or benchmarking against contemporary standards and often overlooks under-represented populations, who tend to bear the greatest burden of diabetes and its complications.

In the present report, we present reporting guidelines for clinically relevant precision medicine research, using common cardiometabolic diseases as the example. We first evaluated a representative sample of the literature on precision medicine in cardiometabolic diseases, determining that the quality of evidence reporting is low, akin to the level previously observed for precision diabetes medicine 3 . We then generated consensus guidelines and a corresponding checklist for reporting of research germane to precision medicine. The purpose of these guidelines is to improve reporting standards so that: (1) evidence can be combined and synthesized in a way that yields meaningful insights from collective efforts; (2) claims of clinical utility can be benchmarked against contemporary standards; and (3) end-user engagement and health equity will be strengthened.

Scoping review

The literature search focused on identifying precision medicine publications using the term ‘precision medicine’ and associated proxy nomenclature, among other keywords and phrases ( Supplementary Methods ). The search identified 2,679 publications, of which 13 were excluded owing to duplication. The remaining 2,666 papers were screened, of which 47 were randomly selected (through computer-generated, random-number sequence) for full text review and quality assessment. The summary (count and percentage) of each quality assessment item across all papers and the quality assessment results for each paper are shown in Supplementary Tables 2 and 3 . This quality assessment yielded a median score of 6 (interquartile range = 4–7) with none of the papers achieving a positive quality evaluation across all 11 items (Fig. 1 ).

Median scores of 47 published precision medicine manuscripts randomly selected for full text review and quality assessment through computer-generated, random-number sequence. IQR, Interquartile range.

A summary of the itemized evidence reporting quality is shown in Supplementary Table 2 . Most abstracts (81%) reported findings relevant to the four pillars of precision medicine (prevention, diagnosis, treatment and/or prognosis) and provided sufficient detail in the methods sections to determine whether the study was designed to test hypotheses on precision medicine (77%), details about participant eligibility (75%) and descriptions of standard reporting definitions (70%). The items that were less frequently reported were the description of patient and public involvement and engagement (PPIE) in determining the impact and utility of precision medicine (15%), the inclusion of the term ‘precision medicine’ in the title or abstract (17%), the reporting of measures of discriminative or predictive accuracy (23%), the description of the approach used to control risk of false-positive reporting (28%), the reporting of effect estimates with 95% confidence intervals and units underlying effect estimates (57%) and the reporting of a statistical test for comparisons of subgroups (for example, interaction test) (60%).

Stakeholder survey

Delphi panel demographics.

Of the 23 Delphi panelists, 22 (96%) completed Delphi survey 1, 18 (78%) and attended the full-panel consensus meeting and 22 (96%) completed Delphi survey 2. All panelists engaged in further extensive dialog around key topics through online communication.

Delphi results

The initial checklist in Delphi survey 1 contained 68 items. After Delphi survey 1 and the full-panel consensus meeting, 2 items were added, resulting in 70 items in Delphi survey 2. At the Consensus meeting, it was determined that the checklist should be used together with existing relevant checklists. These include the CONSORT (Consolidated Standards of Reporting Trials) 17 and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) 18 checklists for interventional trials and observational studies, respectively. This led to a recommendation to remove items covered in established checklists (Supplementary Fig. 1 ). The scoring from Delphi survey 1, Delphi survey 2 and notes from the Consensus meetings are as shown in Supplementary Table 4 . After Delphi survey 2, the consensus was to retain 25 items across 6 core categories.

Guidelines finalization

The executive oversight committee reviewed the panel scores and free-text comments from all the rounds of Delphi surveys to determine the final checklist items and wording. The group discussed five items with inconsistent consensus (between 70% and 80% consensus), resulting in the removal of one item because it overlapped conceptually with another item (17b and 17g in Supplementary Table 4 ). It was also determined that ‘health equity’ should be included as an overarching theme, thereby encouraging users of the checklist to consider this topic more broadly when describing precision medicine research. This resulted in removal of two items.

The final checklist comprised 23 items that the executive oversight committee concluded are unique and essential for reporting standards in precision medicine. The final BePRECISE checklist is presented in Table 1 , with a downloadable version of the checklist available online ( https://www.be-precise.org , and https://www.equator-network.org/reporting-guidelines/ ).

Explanation of checklist Items

The checklist and the explanation of each item are presented in Table 1 . The BePRECISE checklist is intended to complement existing guidelines such as CONSORT 17 , STROBE 18 and PRISMA (Preferred Reporting System for Systematic Reviews and Meta-Analyses) 19 .

These reporting guidelines use the terms ‘precision medicine’ and ‘personalized medicine’ as defined in the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 , as follows:

‘Precision medicine focuses on minimizing errors and improving accuracy in medical decisions and health recommendations. It seeks to maximize efficacy, cost-effectiveness, safety, access for those in need and compliance compared with contemporary evidence-based medicine. Precision medicine emphasizes tailoring diagnostics or therapeutics (prevention or treatment) to subgroups of populations sharing similar characteristics.’

Personalized medicine refers to ‘the use of a person’s own data to objectively gauge the efficacy, safety, and tolerability of therapeutics, and, subjectively, to tailor health recommendations and/or medical decisions to the individual’s preferences, circumstances, and capabilities’.

Accordingly, personalized medicine can be viewed as being nested within the broader concept of precision medicine.

Equity and PPIE (E1–E4)

Equity, diversity and inclusivity considerations and the involvement of patients and public is a crosscutting theme in this checklist. Where relevant, papers should include a description of how equity has been considered, including diversity and inclusivity of study participants, and whether there was PPIE. Cohort selection biases and probable risks when extrapolating the study’s results to other populations should be clearly described.

The selection of participants should consider racial, ethnic, ancestral, geographic and sociodemographic characteristics 20 , and include an explanation for the inclusion or exclusion of groups that are typically under-represented in clinical research (E1 and E2). Race and ethnicity are social constructs but, as they are categories recognized by some government and health authorities in contexts that are relevant to precision medicine, we have elected to retain inclusion of these somewhat controversial terms here.

PPIE in any part of the study should be described, including but not limited to design, conduct and reporting (E3).

Where possible, and ideally with guidance from those with lived experience, the potential impact of the research findings on the target population(s) should be discussed (E4). Consider co-writing these aspects with PPIE representatives.

Title and abstract (1.1–1.4)

In the title and/or abstract, the term ‘precision medicine’ should be included to highlight that the research is relevant to precision medicine (1.1). Given that precision medicine is an approach that can be used in several research contexts, the study design (for example, randomized clinical trial (RCT), retrospective observational) and the research question should be stated clearly (1.2). Use of the terms ‘prevention’, ‘diagnostics’, ‘treatment’ or ‘prognostics’ is needed to highlight which pillar of precision medicine the study concerns 3 (1.3). To ensure transparency about generalizability and/or applicability of the findings to a specific population or subgroup, the study population must be described (1.4).

Background and objectives (2.1–2.2)

The background should clearly describe the rationale for the chosen precision medicine approach, including the context and prior work that led to it and the specific hypothesis being tested (2.1). To provide the reader with greater context, papers should also state the nature and objective of the precision medicine study as ‘etiological’, ‘discovery’, ‘predictive’ and/or ‘confirmatory’ (2.2).

Methods (general)

Although this reporting guide focuses on clarifying elements of papers that are germane to precision medicine, authors are strongly encouraged to ensure that methods also adhere to other appropriate reporting guidelines (for example, CONSORT and STROBE), with the overarching goal of ensuring that the study protocol described therein could, in principle, be accurately reproduced by third-party investigators.

Methods (3.1–3.7)

Methods should describe the aspects of a study design relating to precision medicine in such detail that the design can be understood and replicated (3.1). The rationale for the choice of primary outcome should be clearly stated (3.2).

To enable readers to assess bias and interpret the study findings, this section should state how the participants were identified and enrolled in the study (4.1) and (if applicable) how a subset of a broader group of participants was selected from an existing study (3.3). Any markers used for stratification or prediction should be explicitly stated with an explanation of how the marker(s) was(were) chosen (3.4).

The sample size and how it was derived should be described, for example, following a priori power calculations, or if the sample size was limited primarily by availability or cost, and any implications that this might have for type 2 error (3.5). Authors should also describe attempts to minimize false-positive discovery, especially when multiple testing has occurred (3.5).

If any replication and/or validation analyses were undertaken, a clear description should be given of the approach, including whether these analyses were planned and relevant datasets identified before or after conclusion of primary analyses (3.6), in addition to justification for the sample size and choice of replication cohort (3.7).

Results (4.1–4.4)

The number of participants in the study should be provided, along with a table of baseline characteristics (4.1). If the analysis involves comparison (rather than discovery) of subgroups, the baseline characteristics and numbers of participants should be provided by the subgroup.

Results from any statistical tests done should be reported. Any comparisons of subgroups should include appropriate test statistics, which may include tests of interaction and heterogeneity, and in cluster analyses tests of probability for cluster assignment (for example, relative entropy statistic) (4.2).

Key findings should be benchmarked against current reference standards or practice, if they exist, so that the reader can determine the likely benefit of translating the study’s findings into clinical practice. This may include, for example, the comparison of the new and existing approaches using tests of discriminative (cross-sectional) or predictive (prospective) accuracy, or estimation of net reclassification or changes in numbers needed to treat. If benchmarking has not been done, a clear explanation should be given (4.3).

If validation and/or replication analyses were undertaken, the results of all such attempts at analyses should be clearly described (4.4).

Discussion (5.1–5.2)

The paper should include a balanced and nuanced discussion of any limitations to the interpretation and/or implementation of the reported findings. The limitations section should consider biases that might prevent fair and equitable generalization of the study’s findings to other populations, particularly to groups that are under-represented within the published literature. Authors are also encouraged to consider other potential biases that might arise with stratified and subgroup analyses (5.1).

If there is a direct clinical implication of the study’s findings, authors should describe how their findings might be applied in clinical practice. This might, for example, include an explanation of how any algorithms, technologies or risk markers that stem directly from the research might benefit clinical practice.

The BePRECISE guidelines are intended to enhance publication of research on precision medicine by improving quality and standardization of reporting. In turn, it is anticipated that this will help improve and accelerate the impact of precision medicine research on the health and well-being of target populations and individuals.

BePRECISE was initiated to follow up on recommendations from the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The report, founded on 16 systematic evidence reviews summarizing research described in >100,000 published papers, found a low degree of standardization across the published literature, with a broad absence of key information needed for benchmarking against contemporary standards, validation analyses and meaningful interpretation of research findings.

Implementation of the checklist

These reporting guidelines were derived through structured evaluation and consensus processes undertaken by subject-matter experts in precision medicine for complex traits. The report is premised on cardiometabolic disease translational research but is relevant to translation of research in other complex diseases. These guidelines are directed toward authors describing translational research in precision medicine, as well as for journal editors handling submissions in this field. These guidelines may also be of value to funding agencies, policy advisers and health educators.

The BePRECISE guidelines are designed to be used together with existing study-specific checklists such as CONSORT 17 , STROBE 18 and STORMS (Strengthening the Organization and Reporting of Microbiome Studies) 21 . Publications relevant to precision medicine cover diverse topics and study designs; thus, to accommodate this diversity, we recommend that authors elaborate on relevant details related to checklist items to facilitate manuscript evaluations by journal editors and peer reviewers who will determine whether a given paper has addressed the BePRECISE checklist criteria.

Health equity

Precision medicine has the potential to improve health equity by making health advice and medical therapies more accessible to those in most need and by being more effective and acceptable to the recipient than contemporary clinical approaches. Nevertheless, as the ‘inverse care law’ 22 highlights, the best healthcare often reaches those who need it least. We believe that precision medicine research should place emphasis on the development of solutions for people in greatest need, regardless of who or where they are.

Ensuring representation of underserved populations, where the disease burden can be high, is important because determining the effectiveness of precision medicine solutions requires data from the target populations. Research in population genetics provides clear evidence of this, where the predictive accuracy of polygenic burden scores can be low when applied outside the data-source population, even when these populations are geographically proximal 23 , 24 . Raising awareness of these challenges by discussing them in the health literature and, ultimately, by addressing them through improved study design could facilitate enhancement of health equity using precision medicine approaches.

Promoting equity through precision medicine requires awareness of the many biases. For this reason, the BePRECISE guidelines place emphasis on equity, diversity and inclusion as an overarching concept throughout the checklist.

As with health equity, the BePRECISE guidelines position PPIE as a crosscutting theme to motivate its consideration in all elements of precision medicine translational research. We encourage those using the BePRECISE checklist to follow existing guidance on PPIE 25 . Ensuring that the eventual recipients of precision medicine solutions are adequately represented in the planning, execution and reporting of precision medicine research will help maximize the translational value of the research. Ideally, research teams should include members of the communities that will eventually benefit from this work, including in leadership roles, although to achieve this will often require long-term capacity strengthening. This engagement will help ensure that the relevance and utility of the research output are maximized. It will also strengthen the potential for target populations to determine their own health trajectories. Where this is not immediately achievable, establishing authentic partnerships with representatives from these target populations should be prioritized. This may involve community consultations, training opportunities and co-creation of research proposals with assigned community members, through dissemination and translation of research findings. Moreover, the selection of study participants should be done equitably and result in study cohorts that are representative of the populations who are the focus of the research 26 . The use of patient-reported outcome measures and patient-reported experience measures should be considered during the research design and execution phases, and reported in research papers wherever possible following established guidelines 27 , 28 . Doing so will amplify the patient voice and maximize the relevance of the research to the target populations and individuals.

Cost-effectiveness

The translation of precision medicine research into practice will invariably depend on it being cost-effective, affordable and accessible. This initial version of the BePRECISE checklist does not include checklist items pertaining directly to these important factors. The consensus view was that such analyses are sufficiently complex to stand alone and are likely to be outside the scope of most current precision medicine research. This topic may be revisited in subsequent versions of the checklist.

Strengths and limitations

We believe that implementation of the BePRECISE checklist in the context of academic publishing will strengthen standardization of reporting across precision medicine research, ultimately enabling improved and equitable translation of research findings into the clinical and public health settings. The checklist will also encourage investigators to improve study design, particularly with respect to health equity. Other strengths include rigor of our consensus methods and the diverse range of societal backgrounds and expertise of our group.

We acknowledge that precision medicine in many complex diseases is relatively nascent (with the exception of precision oncology), with the needs of the field and stakeholders evolving. We plan to evaluate uptake of the checklist among journals and authors to assess whether items should be added or removed from the checklist as the field matures. An additional limitation is that the BePRECISE consensus group is small by comparison with similar efforts in other fields of research. We will involve a larger group of experts with broader global and technical representation in future efforts, including increased representation from low- and middle-income countries and individuals with more diverse lived experiences. Additional technical expertise may also be needed from other disciplines, including health economics and health systems administration, for example.

We acknowledge that journal formatting requirements and procedures may not always entirely align with the checklist specifications. We removed a checklist item for provision of a plain language summary, for example, because many journal formats are presently unable to accommodate this type of additional material. However, we hope that in the future editors and publishers of medical and scientific journals will include space for this incredibly important component that facilitates scientific communication with the public.

We defer to editorial and reviewer discretion in implementation of the BePRECISE checklist. Although the BePRECISE checklist items are included to support best scientific practices, at least in the short term, some ongoing precision medicine studies will not have addressed the health equity or PPIE considerations in their design. We do not expect that insufficient attention to these items would be a sole reason for not considering a manuscript for review, unless blatant disregard for participant and/or community safety, privacy or respect has occurred in the study design and/or conduct. Over time, however, we hope that health equity and PPIE will be considered as standard practice in precision medicine research and implementation.

Conclusions

The BePRECISE reporting guidelines have been generated through a structured consensus process to address the need for better reporting of clinical translational research in precision medicine in common complex diseases. The burgeoning literature on this topic is reported inconsistently, impeding the assimilation, syntheses and interpretation of evidence. There is a general lack of benchmarking against contemporary standards, a situation that makes it impossible to determine whether new precision medicine approaches might be beneficial, feasible and sustainable. Moreover, very little existing precision medicine research has incorporated PPIE or focused on the groups within societies most in need of innovative precision medicine solutions. These barriers limit the positive impact that precision medicine could have on the health and well-being of those most in need. The BePRECISE reporting guidelines are intended to help address these and other important challenges.

Consortium structure

The BePRECISE Consortium comprised an executive oversight committee (S.S.L., Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F.), which oversaw the full process, with representation across key domain areas, and an evidence evaluation group (Z.S.-A., M.L.M., A.A., H.F., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., S.S.R., J.L.S. and P.W.F.), which undertook the scoping review to determine current reporting standards. All consortium members participated in a Delphi consensus process 29 . The Consortium chair and co-chair were P.W.F. and S.S.L., respectively (Supplementary Table 1 ).

Protocols and registrations

A scoping review protocol was developed before initiating the literature review or consensus activities and was registered in the Open Science Framework (http://osf-registrations-nh4g2-v1). The consensus process followed the EQUATOR (Enhancing the QUAlity and TRansparency of health Research) Network recommendations for reporting guidelines development ( https://www.equator-network.org/library/equator-network-reporting-guideline-manual ) and was registered with EQUATOR as ‘Reporting guidelines under development’ ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs ). The final BePRECISE guidelines are available on the Equator website ( https://www.equator-network.org/reporting-guidelines/ ).

The purpose of the scoping review was to determine whether the published literature on precision medicine in cardiometabolic diseases met a minimum threshold for reporting quality. We set the minimum expectation as a condition where most (that is, ≥50%) published papers in this domain are adequately reported. To define a study as adequately or inadequately reported (as a binary variable), members of the scoping review committee identified, through consultation, 11 key items (Supplementary Tables 2 and 3 ). Papers that met all 11 reporting criteria were deemed, a priori, to be adequately reported.

The checklist items used to assess the reporting quality of studies captured in the scoping review were determined before the Delphi surveys were undertaken. These scoping review checklist items correspond with some of those used in the Delphi surveys that formed the basis of the final BePRECISE checklist, because both the scoping review and Delphi surveys are, to varying degrees, derived from the findings of the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The scoping review was intended to provide a snapshot of the quality of reporting in a subset of literature relating to precision medicine. It was not undertaken to inform the items in the BePRECISE checklist; this purpose was served by the systematic evidence reviews 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 and the Consensus report 3 described above.

Based on the findings of the precision diabetes medicine Consensus report 3 , we hypothesized that no more than 30% of currently published studies are adequately reported. This assumption was tested by full text reviewing a statistically powered, random subsample of published papers on precision medicine across cardiometabolic diseases (ʻSearch strategyʼ and ʻSample size estimationʼ). This scoping review was conducted in accordance with the PRISMA Extension for Scoping Review guidelines 30 to identify and assess the current literature on precision medicine in cardiometabolic diseases and was completed before the ‘guidelines consensus process’ described below.

Sample size estimation

The literature search was not intended to be a comprehensive evaluation of the published evidence, but instead to provide an unbiased representation of this literature. To determine how many papers should be reviewed as a representative sample of the published literature, an a priori sample size calculation was performed using SAS software v.9.4 (SAS Institute). Given the scenario described, we used a two-sided test with a type 1 error threshold (critical α) of 0.05, assuming a null hypothesis proportion of 0.50, which corresponds to our minimum expectation, an expected number of adequately reported papers of <30% and nominal power of 80%. This calculation determined that 47 randomly selected papers should be full text reviewed to ascertain whether the assumed proportion of adequately reported studies is significantly lower than the prespecified null proportion (that is, to infer that the quality of papers reported in this field is lower than the minimum expectation).

Search strategy

We searched the PubMed database ( https://pubmed.ncbi.nlm.nih.gov ) to identify relevant articles published in the past 5 years (January 2019 to January 2024). The search strategy incorporated keywords and terms ( https://www.ncbi.nlm.nih.gov/mesh ) in human epidemiological cohorts and clinical trials representing: (1) precision medicine, (2) cardiometabolic diseases and (3) clinical translation (see Supplementary Methods for the detailed search strategy). The search was constrained to publications written in English. Conference abstracts, case reports, study protocols, reviews and animal studies were excluded.

Study selection and quality assessment

Covidence software ( https://www.covidence.org ; Veritas Health Innovation) was used to manage the scoping review selection process. Studies were filtered in three stages: (1) removal of duplicate publications; (2) ascertainment of study eligibility based on title and abstract by at least two independent reviewers; and (3) full text review of 47 randomly selected studies, where at least 2 independent reviewers assessed the eligibility of each publication according to the inclusion and exclusion criteria. Each paper was further evaluated to determine whether it met the 11 predetermined quality criteria. Any conflicts were subsequently resolved by an independent reviewer.

Consensus process

The five-step consensus process was based on a modified Delphi and nominal group technique 29 . The consensus process involved: (1) completion of an initial Delphi survey (6–13 February 2024); (2) a consensus meeting (15–16 February 2024); and (3) a second Delphi survey (19–26 February 2024). Finalization of the checklist was conducted at a second consensus meeting by the executive oversight committee (5–6 March 2024), who reviewed the voting of all rounds of the Delphi survey, made final decisions about item inclusion and refined wording of the BePRECISE checklist. The executive oversight committee also evaluated the checklist against two publications on precision medicine determined through the scoping review to be of high and low quality, respectively. The final version of the checklist was circulated to all panel members for consultation and approval (13–19 March 2024).

The items in the first iteration of Delphi survey 1 were derived from existing checklists: CONSORT 17 , STROBE 18 , CONSORT-Equity 2017 extension 31 and STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE guidelines 32 . Additional items specific to precision medicine were generated based on the reporting gaps identified from the series of systematic reviews (11 published) that underpinned the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The draft of Delphi survey 1 was presented to the full panel at a roundtable discussion followed by co-development with the full panel through an online document-sharing platform. The final items for Delphi survey 1, including the input sources for its development, are shown in Supplementary Table 3 .

The Delphi survey response scale had five options: ‘Completely inappropriate’, ‘Somewhat inappropriate’, ‘Neither appropriate nor inappropriate’, ‘Somewhat appropriate’ and ‘Completely appropriate’. The consensus threshold was defined a priori as at least 80% of the panel voting for ‘Completely appropriate’ or ‘Somewhat appropriate’. Items with voting scores under this consensus threshold were discussed at the Consensus meetings. The Delphi surveys were administered online and were anonymous. Panelists were invited to provide free-text comments to suggest new items (survey 1 only), suggest a change of wording for a given item or justify their voting decision. The voting scores and anonymous comments for each item from the previous consensus round were provided to panelists at the subsequent rounds, such that consensus was reached iteratively.

Delphi panel and executive oversight committee

The BePRECISE checklist panelists cover the core areas of expertise outlined in the EQUATOR Network recommendations for reporting guidelines development ( https://www.equator-network.org/library/equator-network-reporting-guideline-manual ). The panel includes subject-matter experts across relevant disease areas and with expertise in the topics highlighted as gaps in the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’. Moreover, the BePRECISE panelist selection focused on ensuring diversity: (1) global representation (Europe, North America, sub-Saharan Africa and Australia); (2) career stages (23% early career researchers within 10 years of research experience, 27% of mid-career researchers of 11–15 years of experience and 50% of senior researchers of >20 years of experience); and (3) gender (55% of authors being female).

Accordingly, the Delphi panel comprised subject-matter experts in key cardiometabolic disorders (diabetes, obesity, cardiovascular disease, fatty liver disease, renal disease), statistics, study design (epidemiologists and clinical trialists), journal editorial, lived experience, benchmarking and technology, education and translation, health equity, community engagement and clinical practice. Several of these experts are based in or have worked extensively with investigators in low- and middle-income countries (M.R., N.S., J.L.S. and P.W.F.).

The executive oversight committee for this report consisted of multidisciplinary experts in cardiometabolic disorders, equity research, medical journal editorial and lived experience (P.W.F., S.S.L., S.S.R., J.L.S., A.H.N., Z.S.A. and M.L.M.).

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Acknowledgements

As a PPIE representative from Australia, A.H.N. was remunerated by the Cardiometabolic Health Implementation Research in Postpartum women (CHIRP) consumer group, Eastern Health Clinical School, Monash University according to the Monash Partners Remuneration and Reimbursement Guidelines for Consumer and Community Involvement Activity. The Covidence license was paid for in part by Lund University’s Medical Library (Faculty of Medicine, Lund University, Lund, Sweden). Z.S.-A. was supported by the Canadian Institutes of Health Research Fellowship; M.L.M. by the Italian Ministry of Health Grant ‘Ricerca Finalizzata 2019’ (no. GR-2019-12369702); A.A. by Swedish Heart–Lung Foundation (grant no. 20190470), Swedish Research Council (2018-02837), EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD) and HORIZON-RIA project (grant no. 101095146—PRIME-CKD); H.F. by EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD) and HORIZON-RIA project (grant no. 101095146—PRIME-CKD). J.M.D. is a Wellcome Trust Early Career Fellow (no. 227070/Z/23/Z) and is supported by the Medical Research Council (UK) (grant no. MR/N00633X/1) and the National Institute for Health and Care Research (NIHR), Exeter Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. R.J.F.L. is employed at the Novo Nordisk Foundation Center for Basic Metabolic Research, which is supported by grants from the Novo Nordisk Foundation (nos. NNF23SA0084103 and NNF18CC0034900), and in addition by personal grants from the Novo Nordisk Foundation (Laureate award no. NNF20OC0059313) and the Danish National Research Fund (Chair DNRF161). M.R. is a South African Research Chair on the Genomics and Bioinformatics of African Populations, funded by the Department of Science and Innovation. N.S. is Chair of the Obesity Mission for the Office of Life Science, UK Government. M.F.G. is supported by the Swedish Research Council (EXODIAB, no. 2009-1039), Swedish Foundation for Strategic Research (LUDC-IRC, no. 15-0067) and EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD). A.H.N’.s salary is supported by funding from the Medical Research Future Fund and Monash Centre for Health Research and Implementation. S.S.R. is supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (no. R01 DK122586), the Juvenile Diabetes Research Foundation (no. 2-SRA-202201260-S-B) and the Leona M. and Harry B. Helmsley Charitable Trust (no. 2204–05134). P.W.F. is supported by grants from the Swedish Research Council (no. 2019-01348), the European Commission (ERC-2015-CoG-681742-NASCENT), and Swedish Foundation for Strategic Research (no. LUDC-IRC, 15-0067).

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Health Systems and Equity, Eastern Health Clinical School, Monash University, Box Hill, Victoria, Australia

Siew S. Lim & Jacqueline Boyle

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Zhila Semnani-Azad, Deirdre K. Tobias & Paul W. Franks

Unit of Metabolic Disease, University-Hospital of Padua, Padua, Italy

Mario L. Morieri

Department of Medicine, University of Padua, Padua, Italy

Monash Centre for Health Research Implementation, Monash University and Monash Health, Melbourne, Victoria, Australia

Ashley H. Ng

Monash Partners Academic Health Science Centre, Melbourne, Victoria, Australia

Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

Ashley H. Ng & Claudia Langenberg

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Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK

John M. Dennis

Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany

Claudia Langenberg

Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Ruth J. F. Loos & Jordi Merino

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Ruth J. F. Loos

Diabetes Australia, Canberra, Australian Capital Territory, Australia

Melinda Morrison

Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa

Michele Ramsay

Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University School of Medicine, Richmond, VA, USA

Arun J. Sanyal

School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK

Naveed Sattar

Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA

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Jordi Merino

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Deirdre K. Tobias

Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK

Michael I. Trenell

Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

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S.S.L. (co-chair), Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F. (chair) formed the executive oversight committee. Z.S.-A. (lead), M.L.M., A.A., H.F., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., S.S.R., J.L.S. and P.W.F. formed the evidence evaluation group. S.S.L. (lead), Z.S.-A., M.L.M., A.A., H.F., J.B., C.C., J.M.D., C.L., R.J.F.L., M.M., M.R., A.J.S., N.S., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., A.H.N., S.S.R., J.L.S. and P.W.F. formed the consensus review panel. A.H.N. and C.C. were the PPIE representatives. S.S.L., Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F. wrote the first draft of the manuscript. All the authors edited and approved the final version of the manuscript before submission for journal review.

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Correspondence to Paul W. Franks .

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Competing interests.

M.L.M. has consulted for and/or received speaker honoraria from Amarin, Amgen, AstraZeneca, Boehringer Ingelheim, Daichi, Eli Lilly, Merck Sharp & Dohme, Novo Nordisk, Novartis and Servier. In the past 5 years, A.H.N. has received an investigator-initiated grant from Abbott Diabetes Care and consulting honoraria from Roche Diabetes Care, Australia and the Australian Diabetes Educators Association. There are no perceived conflicts from previous involvements on this work. C.C. is a member of the Board of Directors for the Steno Diabetes Center in Copenhagen, Denmark. The views expressed in this paper do not necessarily reflect those of the Steno Center. M.R. is a consultant on the Genentech. ‘One Roche: Race, Ethnicity and Ancestry (“REA”) Initiative’. A.J.S. received research grants (paid to the institution) from: Intercept, Lilly, Novo Nordisk, Echosense, Boehringer Ingelhiem, Pfizer, Merck, Bristol Myers Squibb, Hanmi, Madrigal, Galmed, Gilead, Salix and Malinckrodt; was a consultant for Intercept, Gilead, Merck, NGM Bio, Terns, Regeneron, Alnylam, Amgen, Genentech, Pfizer, Novo Nordisk, AstraZeneca, Salix, Malinckrodt, Lilly, Histoindex, Path AI, Rivus, Hemoshear, Northsea, 89Bio, Altimmune, Surrozen and Poxel; and had ownership interests in Tiziana, Durect, Exhalenz, GENFIT, Galmed, Northsea and Hemoshear. N.S. has consulted for and/or received speaker honoraria from Abbott Laboratories, AbbVie, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Menarini-Ricerche, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics and Sanofi; and received grant support (paid to the institution) from AstraZeneca, Boehringer Ingelheim, Novartis and Roche Diagnostics outside the submitted work. M.F.G. received financial and nonfinancial (in-kind) support (paid to the institution) from Boehringer Ingelheim Pharma, JDRF International, Eli Lilly, AbbVie, Sanofi-Aventis, Astellas, Novo Nordisk, Bayer, within EU grant H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD); also received financial and in-kind support from Novo Nordisk, Pfizer, Follicum, Coegin Pharma, Abcentra, Probi and Johnson & Johnson, within a project funded by the Swedish Foundation for Strategic Research on precision medicine in diabetes (LUDC-IRC no. 15-0067); and received personal consultancy fees from Lilly and Tribune Therapeutics AB. M.I.T. has, within the past 5 years, received consulting/honoraria from the Novo Nordisk Foundation, Abbott Nutrition, Changing Health and DAISER. This work is independent and does not represent the opinions of these organizations. S.S.R. has received consulting honoraria from Westat and investigator-initiated grants from the US National Institutes of Health, the Juvenile Diabetes Research Foundation and the Leona M. and Harry B. Helmsley Charitable Trust. J.L.S. receives consulting fees from the World Health Organization and the University of Bergen. This work was done outside these roles and the opinions expressed in these guidelines do not necessarily reflect those of the World Health Organization or the University of Bergen. J.L.S. was deputy editor of Nature Medicine until December 2023. She left employment at Springer Nature before any of her work on this Consensus Statement was initiated. P.W.F. was an employee of the Novo Nordisk Foundation at the time that these guidelines were written, although this work was done entirely within his academic capacity. The opinions expressed in these guidelines do not necessarily reflect those of the Novo Nordisk Foundation. Within the past 5 years, he has received consulting honoraria from Eli Lilly, Novo Nordisk Foundation, Novo Nordisk, UBS and Zoe, and previously had other financial interests in Zoe. He has also received investigator-initiated grants (paid to the institution) from numerous pharmaceutical companies as part of the Innovative Medicines Initiative of the European Union. The remaining authors declare no competing interests. J.A.B. received royalties from Elsevier as an editor on a medical textbook that does not impact this work. A.J.S. has stock options in Rivus, is a consultant to Boehringer Ingelhiem and Akero, and has grants from Takeda.

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Lim, S.S., Semnani-Azad, Z., Morieri, M.L. et al. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist. Nat Med 30 , 1874–1881 (2024). https://doi.org/10.1038/s41591-024-03033-3

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Received : 21 March 2024

Accepted : 29 April 2024

Published : 19 July 2024

Issue Date : July 2024

DOI : https://doi.org/10.1038/s41591-024-03033-3

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