Charting Diagnostic Safety: Exploring Patient-Provider Discordance in Medical Record Documentation.
OpenNotes
communication
diagnostic errors
patient experience
patient safety
Journal
Journal of general internal medicine
ISSN: 1525-1497
Titre abrégé: J Gen Intern Med
Pays: United States
ID NLM: 8605834
Informations de publication
Date de publication:
05 Sep 2024
05 Sep 2024
Historique:
received:
11
03
2024
accepted:
13
08
2024
medline:
6
9
2024
pubmed:
6
9
2024
entrez:
5
9
2024
Statut:
aheadofprint
Résumé
The 21st Century Cures Act enables patients to access their medical records, thus providing a unique opportunity to engage patients in their diagnostic journey. To explore the concordance between patients' self-reported diagnostic concerns and clinician-interpreted information in their electronic health records. We conducted a mixed-methods analysis of a cohort of 467 patients who completed a structured data collection instrument (the Safer Dx Patient) to identify diagnostic concerns while reviewing their clinician's notes. We conducted a qualitative content analysis of open-ended responses on both the tools and the case summaries. Two clinical chart reviewers, blinded to patient-reported diagnostic concerns, independently conducted chart reviews using a different structured instrument (the Revised Safer Dx Instrument) to identify diagnostic concerns and generate case summaries. The primary outcome variable was chart review-identified diagnostic concerns. Multivariate logistic regression tested whether the primary outcome was concordant with patient-reported diagnostic concerns. Geisinger, a large integrated healthcare organization in rural and semi-urban Pennsylvania. Cohort of adult patients actively using patient portals and identified as "at-risk" for diagnostic concerns using an electronic trigger algorithm based on unexpected visit patterns in a primary care setting. In 467 cohort patients, chart review identified 31 (6.4%) diagnostic concerns, of which only 11 (21.5%) overlapped with 51 patient-reported diagnostic concerns. Content analysis revealed several areas of discordant understanding of the diagnostic process between clinicians and patients. Multivariate logistic regression analysis showed that clinician-identified diagnostic concerns were associated with patients who self-reported "I feel I was incorrectly diagnosed during my visit" (odds ratio 1.65, 95% CI 1.17-2.3, p < 0.05). Patients and clinicians appear to have certain differences in their mental models of what is considered a diagnostic concern. Efforts to integrate patient perspectives and experiences with the diagnostic process can lead to better measurement of diagnostic safety.
Sections du résumé
BACKGROUND
BACKGROUND
The 21st Century Cures Act enables patients to access their medical records, thus providing a unique opportunity to engage patients in their diagnostic journey.
OBJECTIVE
OBJECTIVE
To explore the concordance between patients' self-reported diagnostic concerns and clinician-interpreted information in their electronic health records.
DESIGN
METHODS
We conducted a mixed-methods analysis of a cohort of 467 patients who completed a structured data collection instrument (the Safer Dx Patient) to identify diagnostic concerns while reviewing their clinician's notes. We conducted a qualitative content analysis of open-ended responses on both the tools and the case summaries. Two clinical chart reviewers, blinded to patient-reported diagnostic concerns, independently conducted chart reviews using a different structured instrument (the Revised Safer Dx Instrument) to identify diagnostic concerns and generate case summaries. The primary outcome variable was chart review-identified diagnostic concerns. Multivariate logistic regression tested whether the primary outcome was concordant with patient-reported diagnostic concerns.
SETTING
METHODS
Geisinger, a large integrated healthcare organization in rural and semi-urban Pennsylvania.
PARTICIPANTS
METHODS
Cohort of adult patients actively using patient portals and identified as "at-risk" for diagnostic concerns using an electronic trigger algorithm based on unexpected visit patterns in a primary care setting.
RESULTS
RESULTS
In 467 cohort patients, chart review identified 31 (6.4%) diagnostic concerns, of which only 11 (21.5%) overlapped with 51 patient-reported diagnostic concerns. Content analysis revealed several areas of discordant understanding of the diagnostic process between clinicians and patients. Multivariate logistic regression analysis showed that clinician-identified diagnostic concerns were associated with patients who self-reported "I feel I was incorrectly diagnosed during my visit" (odds ratio 1.65, 95% CI 1.17-2.3, p < 0.05).
CONCLUSION
CONCLUSIONS
Patients and clinicians appear to have certain differences in their mental models of what is considered a diagnostic concern. Efforts to integrate patient perspectives and experiences with the diagnostic process can lead to better measurement of diagnostic safety.
Identifiants
pubmed: 39237788
doi: 10.1007/s11606-024-09007-y
pii: 10.1007/s11606-024-09007-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Références
Lorkowski J, Pokorski M. Medical Records: A Historical Narrative. Biomedicines. 2022;10(10):2594. https://doi.org/10.3390/biomedicines10102594
doi: 10.3390/biomedicines10102594
pubmed: 36289856
Colicchio TK, Cimino JJ, Fiol GD. Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era. J Med Internet Res. 2019;21(6):e13313. https://doi.org/10.2196/13313
doi: 10.2196/13313
pubmed: 31162125
pmcid: 6682280
Blease CR, Bell SK. Patients as diagnostic collaborators: sharing visit notes to promote accuracy and safety. Diagnosis. 2019;6(3):213-221. https://doi.org/10.1515/dx-2018-0106
doi: 10.1515/dx-2018-0106
pubmed: 31039128
Bell SK, Delbanco T, Elmore JG, et al. Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes. JAMA Netw Open. 2020;3(6):e205867-e205867. https://doi.org/10.1001/jamanetworkopen.2020.5867
doi: 10.1001/jamanetworkopen.2020.5867
pubmed: 32515797
pmcid: 7284300
Skinner KM, Miller DR, Lincoln E, Lee A, Kazis LE. Concordance Between Respondent Self-reports and Medical Records for Chronic Conditions: Experience From the Veterans Health Study. J Ambulatory Care Manage. 2005;28(2):102. Accessed August 31, 2023. https://journals.lww.com/ambulatorycaremanagement/abstract/2005/04000/concordance_between_respondent_self_reports_and.2.aspx
Valikodath NG, Newman-Casey PA, Lee PP, Musch DC, Niziol LM, Woodward MA. Agreement of Ocular Symptom Reporting Between Patient-Reported Outcomes and Medical Records. JAMA Ophthalmol. 2017;135(3):225-231. https://doi.org/10.1001/jamaophthalmol.2016.5551
doi: 10.1001/jamaophthalmol.2016.5551
pubmed: 28125754
pmcid: 5404734
Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013;20(1):144-151. https://doi.org/10.1136/amiajnl-2011-000681
doi: 10.1136/amiajnl-2011-000681
pubmed: 22733976
pmcid: 3555312
Weissman JS, Schneider EC, Weingart SN, et al. Comparing Patient-Reported Hospital Adverse Events with Medical Record Review: Do Patients Know Something That Hospitals Do Not? Ann Intern Med. 2008;149(2):100. https://doi.org/10.7326/0003-4819-149-2-200807150-00006
doi: 10.7326/0003-4819-149-2-200807150-00006
pubmed: 18626049
Giardina TD, Haskell H, Menon S, et al. Learning From Patientsts-Reported Hospital Adverse Events with Medical Record Review: Do Patients Know SomethiHealth Aff (Millwood). 2018;37(11):1821–1827. https://doi.org/10.1377/hlthaff.2018.0698
Giardina TD, Korukonda S, Shahid U, et al. Use of Patient Complaints to Identify Diagnosis-Related Safety Concerns: A Mixed-Method Evaluation. BMJ Qual Saf. 2021;Published Online First: 17 February 2021. https://doi.org/10.1136/bmjqs-2020-011593
Matt V, Matthew H. The retrospective chart review: important methodological considerations. J Educ Eval Health Prof. 2013;10:12. https://doi.org/10.3352/jeehp.2013.10.12
doi: 10.3352/jeehp.2013.10.12
pmcid: 3853868
Thomas EJ, Lipsitz SR, Studdert DM, Brennan TA. The Reliability of Medical Record Review for Estimating Adverse Event Rates. Ann Intern Med. 2002;136(11):812-816. https://doi.org/10.7326/0003-4819-136-11-200206040-00009
doi: 10.7326/0003-4819-136-11-200206040-00009
pubmed: 12044129
Yang D, Fineberg HV, Cosby K. Diagnostic Excellence. JAMA. 2021;326(19):1905-1906. https://doi.org/10.1001/jama.2021.19493
doi: 10.1001/jama.2021.19493
pubmed: 34709367
Singh H, Upadhyay DK, Torretti D. Developing Health Care Organizations That Pursue Learning and Exploration of Diagnostic Excellence: An Action Plan. Acad Med. 2020;95(8):1172-1178. https://doi.org/10.1097/ACM.0000000000003062
doi: 10.1097/ACM.0000000000003062
pubmed: 31688035
ONC’s Cures Act Final Rule. Accessed November 30, 2020. https://www.healthit.gov/curesrule/
DesRoches CM, Bell SK, Dong Z, et al. Patients Managing Medications and Reading Their Visit Notes: A Survey of OpenNotes Participants. Ann Intern Med. 2019;171(1):69-71. https://doi.org/10.7326/M18-3197
doi: 10.7326/M18-3197
pubmed: 31132794
Gerard M, Fossa A, Folcarelli PH, Walker J, Bell SK. What Patients Value About Reading Visit Notes: A Qualitative Inquiry of Patient Experiences With Their Health Information. J Med Internet Res. 2017;19(7). https://doi.org/10.2196/jmir.7212
Bell SK, Folcarelli P, Fossa A, et al. Tackling Ambulatory Safety Risks Through Patient Engagement: What 10,000 Patients and Families Say About Safety-Related Knowledge, Behaviors, and Attitudes After Reading Visit Notes. J Patient Saf. 2021 Dec 1;17(8):e791-e799. https://doi.org/10.1097/PTS.0000000000000494 .
doi: 10.1097/PTS.0000000000000494
pubmed: 29781979
Fernández L, Fossa A, Dong Z, et al. Words Matter: What Do Patients Find Judgmental or. Offensive in Outpatient Notes? J Gen Intern Med. 2021;36(9):2571-2578. https://doi.org/10.1007/s11606-020-06432-7
Bell SK, Bourgeois F, Dong J, et al. Patient Identification of Diagnostic Safety Blindspots and Participation in “Good Catches” Through Shared Visit Notes. Milbank Q. 2022;100(4):1121-1165. https://doi.org/10.1111/1468-0009.12593
doi: 10.1111/1468-0009.12593
pubmed: 36539389
pmcid: 9836247
Johnson C, Richwine, Patel V. Individuals Richwine, Patel V. V. 11/1468rtals and Smartphone Health Apps, 2020 | HealthIT.Gov. ONC Data Brief Accessed May 18, 2023. https://www.healthit.gov/data/data-briefs/individuals-access-and-use-patient-portals-and-smartphone-health-apps-2020
Giardina TD, Choi DT, Upadhyay DK, et al. Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes. J Am Med Inform Assoc. 2022;29(6):1091-1100. https://doi.org/10.1093/jamia/ocac036
doi: 10.1093/jamia/ocac036
pubmed: 35348688
pmcid: 9093029
Watkins DC. Secondary Data in Mixed Methods Research (Mixed Methods Research Series). 1st Edition. SAGE Publications, Inc
Al-Mutairi A, Meyer AND, Thomas EJ, et al. Accuracy of the Safer Dx Instrument to Identify Diagnostic Errors in Primary Care. J Gen Intern Med. 2016;31(6):602-608. https://doi.org/10.1007/s11606-016-3601-x
doi: 10.1007/s11606-016-3601-x
pubmed: 26902245
pmcid: 4870415
Dettori JR, Norvell DC. Kappa and Beyond: Is There Agreement? Glob Spine J. 2020;10(4):499-501. https://doi.org/10.1177/2192568220911648
doi: 10.1177/2192568220911648
Gwet KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol. 2008;61(Pt 1):29-48. https://doi.org/10.1348/000711006X126600
doi: 10.1348/000711006X126600
pubmed: 18482474
Singh H, Khanna A, Spitzmueller C, Meyer AND. Recommendations for using the Revised Safer Dx Instrument to help measure and improve diagnostic safety. Diagnosis. 2019;6(4):315-323. https://doi.org/10.1515/dx-2019-0012
doi: 10.1515/dx-2019-0012
pubmed: 31287795
Singh H, Thomas EJ, Khan MM, Petersen LA. Identifying Diagnostic Errors in Primary Care Using an Electronic Screening Algorithm. Arch Intern Med. 2007;167(3):302-308. https://doi.org/10.1001/archinte.167.3.302
doi: 10.1001/archinte.167.3.302
pubmed: 17296888
Johnson DR, Creech JC. Ordinal Measures in Multiple Indicator Models: A Simulation Study of Categorization Error. Am Sociol Rev. 1983;48(3):398-407. https://doi.org/10.2307/2095231
doi: 10.2307/2095231
Norman G. Likert scales, levels of measurement and the he tor Models: A SimulAdv Health Sci Educ Theory Pract. 2010;15(5):625–632. https://doi.org/10.1007/s10459-010-9222-y
Sullivan GM, Artino AR. Analyzing and Interpreting Data From Likert-Type Scales. J Grad Med Educ. 2013;5(4):541-542. https://doi.org/10.4300/JGME-5-4-18
doi: 10.4300/JGME-5-4-18
pubmed: 24454995
pmcid: 3886444
Zumbo BD, Zimmerman DW. Is the selection of statistical methods governed by level of measurement? Can Psychol Can. 1993;34(4):390-400. https://doi.org/10.1037/h0078865
doi: 10.1037/h0078865
Stoltzfus JC. Logistic Regression: A Brief Primer. Acad Emerg Med. 2011;18(10):1099-1104. https://doi.org/10.1111/j.1553-2712.2011.01185.x
doi: 10.1111/j.1553-2712.2011.01185.x
pubmed: 21996075
Morris NS, MacLean CD, Chew LD, Littenberg B. The Single Item Literacy Screener: Evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7(1):21. https://doi.org/10.1186/1471-2296-7-21
doi: 10.1186/1471-2296-7-21
pubmed: 16563164
pmcid: 1435902
Suzuki R, Yajima N, Sakurai K, et al. Association of patientsntsteracy Screener: Evaluation of a trust in their current physician: the TRUMP2-Net study. medRxiv. Published online January 26, 2021:2021.01.25.21250300. https://doi.org/10.1101/2021.01.25.21250300
Suzuki R, Yajima N, Sakurai K, et al. Association of patientsnts101/2021.01.25.21250300tion of a trust in their current physician among Japanese adults. J Gen Intern Med. 2022;37(5):1115–1121.
Oguro N, Suzuki R, Yajima N, et al. The impact that family membersers021.01.25.21250300tion of a trust in their current physician BMC Health Serv Res. 2021;21(1):1122. https://doi.org/10.1186/s12913-021-07172-y
Benkert R, Cuevas A, Thompson HS, Dove-Medows E, Knuckles D. Ubiquitous Yet Unclear: A Systematic Review of Medical Mistrust. Behav Med. 2019;45(2):86-101. https://doi.org/10.1080/08964289.2019.1588220
doi: 10.1080/08964289.2019.1588220
pubmed: 31343961
Meyer AND, Upadhyay DK, Collins CA, et al. A Program to Provide Clinicians with Feedback on Their Diagnostic Performance in a Learning Health System. Jt Comm J Qual Patient Saf. Published online August 29, 2020. https://doi.org/10.1016/j.jcjq.2020.08.014
Yadav S, Kazanji N, K C N, et al. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. J Am Med Inform Assoc. 2017;24(1):140-144. https://doi.org/10.1093/jamia/ocw067
doi: 10.1093/jamia/ocw067
pubmed: 27357831
Weiner SJ, Wang S, Kelly B, Sharma G, Schwartz A. How accurate is the medical record? A comparison of the physician’s note with a concealed audio recording in unannounced standardized patient encounters. J Am Med Inform Assoc JAMIA. 2020;27(5):770-775. https://doi.org/10.1093/jamia/ocaa027
doi: 10.1093/jamia/ocaa027
pubmed: 32330258
Carel H, Kidd IJ. Epistemic injustice in healthcare: a philosophial analysis. Med Health Care Philos. 2014;17(4):529-540. https://doi.org/10.1007/s11019-014-9560-2
doi: 10.1007/s11019-014-9560-2
pubmed: 24740808
Blease C, Salmi L, Rexhepi H, Hägglund M, DesRoches CM. Patients, clinicians and opennotes: information blocking as a case of epistemic injustice. J Med Ethics. 2022;48(10):785–793. https://doi.org/10.1136/medethics-2021-107275