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Appl Clin Inform 2024; 15(04): 733-742
DOI: 10.1055/s-0044-1788330
Research Article

Patient–Clinician Diagnostic Concordance upon Hospital Admission

Alyssa Lam
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Savanna Plombon
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Alison Garber
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Pamela Garabedian
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Ronen Rozenblum
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
Jacqueline A. Griffin
3   Department of Mechanical & Industrial Engineering, Northeastern University, Boston, Massachusetts, United States
,
Jeffrey L. Schnipper
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
Stuart R. Lipsitz
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
David W. Bates
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
Anuj K. Dalal
1   Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
› Author Affiliations Funding This study was supported by the Agency for Healthcare Research and Quality.
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Abstract

Objectives This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician.

Methods Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance.

Results A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 “R-code”) for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p < 0.01), respectively.

Conclusion About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 “R-code” entered as the principal problem and patient-reported lack of confidence may predict patient–clinician nonconcordance early during hospitalization via this approach.

Keywords

diagnostic error - diagnostic safety - acute care

Protection of Human and Animal Subjects

This study was reviewed and approved by the Mass General Brigham Human Research Committee.


Authors' Contributions

All authors have contributed sufficiently and meaningfully to the conception, design, and conduct of the study; data acquisition, analysis, and interpretation; and/or drafting, editing, and revising the manuscript.


Supplementary Material



Publication History

Received: 15 March 2024

Accepted: 16 June 2024

Article published online:
18 September 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Yang D, Fineberg HV, Cosby K. Diagnostic excellence. JAMA 2021; 326 (19) 1905-1906
    CrossrefPubMedGoogle Scholar
  • 2 Committee on Diagnostic Error in Health Care. , Board on Health Care Services,; Institute of Medicine, The National Academies of Sciences, Engineering, and Medicine.; In: Balogh EP, Miller BT, Ball JR. eds. Improving Diagnosis in Health Care. Washington (DC):: National Academies Press (US);; 2015
    Google Scholar
  • 3 Giardina TD, Shahid U, Mushtaq U, Upadhyay DK, Marinez A, Singh H. Creating a learning health system for improving diagnostic safety: pragmatic insights from US health care organizations. J Gen Intern Med 2022; 37 (15) 3965-3972
    CrossrefPubMedGoogle Scholar
  • 4 Shah NR, Gandhi TK, Bates DW. Diagnostic excellence and patient safety: strategies and opportunities. JAMA 2022; 327 (24) 2391-2392
    CrossrefPubMedGoogle Scholar
  • 5 Raffel KE, Kantor MA, Barish P. et al. Prevalence and characterisation of diagnostic error among 7-day all-cause hospital medicine readmissions: a retrospective cohort study. BMJ Qual Saf 2020; 29 (12) 971-979
    CrossrefPubMedGoogle Scholar
  • 6 Bergl PA, Taneja A, El-Kareh R, Singh H, Nanchal RS. Frequency, risk factors, causes, and consequences of diagnostic errors in critically ill medical patients: a retrospective cohort study. Crit Care Med 2019; 47 (11) e902-e910
    CrossrefPubMedGoogle Scholar
  • 7 Motta-Calderon D, Lam A, Kumiko S. et al. Preliminary prevalence estimate of diagnostic error in patients hospitalized on general medicine: analysis of a random stratified sample. J Hosp Med 2021 ;SHM Converge: Abstract 129
    PubMedGoogle Scholar
  • 8 Auerbach AD, Astik GJ, O'Leary KJ. et al. Prevalence and causes of diagnostic errors in hospitalized patients under investigation for COVID-19. J Gen Intern Med 2023; 38 (08) 1902-1910
    CrossrefPubMedGoogle Scholar
  • 9 Auerbach AD, Lee TM, Hubbard CC. et al; UPSIDE Research Group. Diagnostic errors in hospitalized adults who died or were transferred to intensive care. JAMA Intern Med 2024; 184 (02) 164-173
    CrossrefPubMedGoogle Scholar
  • 10 Konieczny KLA, Carr K, Motta-Calderon D. et al. Diagnostic process failures associated with diagnostic error in the hospital. Society of Hospital Medicine Conference,. 2022
    PubMedGoogle Scholar
  • 11 Griffin JA, Carr K, Bersani K. et al. Analyzing diagnostic errors in the acute setting: a process-driven approach. Diagnosis (Berl) 2021; 9 (01) 77-88
    CrossrefPubMedGoogle Scholar
  • 12 Dukhanin V, McDonald KM, Gonzalez N, Gleason KT. Patient reasoning: patients' and care partners' perceptions of diagnostic accuracy in emergency care. Med Decis Making 2024; 44 (01) 102-111
    CrossrefPubMedGoogle Scholar
  • 13 Dahm MR, Crock C. Understanding and communicating uncertainty in achieving diagnostic excellence. JAMA 2022; 327 (12) 1127-1128
    CrossrefPubMedGoogle Scholar
  • 14 Santhosh L, Chou CL, Connor DM. Diagnostic uncertainty: from education to communication. Diagnosis (Berl) 2019; 6 (02) 121-126
    CrossrefPubMedGoogle Scholar
  • 15 Cifra CL, Custer JW, Smith CM. et al. Prevalence and characteristics of diagnostic error in pediatric critical care: a multicenter study. Crit Care Med 2023; 51 (11) 1492-1501
    CrossrefPubMedGoogle Scholar
  • 16 Schnock KO, Garber A, Fraser H. et al. Providers' and patients' perspectives on diagnostic errors in the acute care setting. Jt Comm J Qual Patient Saf 2023; 49 (02) 89-97
    PubMedGoogle Scholar
  • 17 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 (06) 1091-1100
    CrossrefPubMedGoogle Scholar
  • 18 Giardina TD, Haskell H, Menon S. et al. Learning from patients' experiences related to diagnostic errors is essential for progress in patient safety. Health Aff (Millwood) 2018; 37 (11) 1821-1827
    CrossrefPubMedGoogle Scholar
  • 19 Garber A, Garabedian P, Wu L. et al. Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach. JAMIA Open 2023; 6 (02) ooad031
    CrossrefPubMedGoogle Scholar
  • 20 Schnipper JL, Raffel KE, Keniston A. et al. Achieving diagnostic excellence through prevention and teamwork (ADEPT) study protocol: a multicenter, prospective quality and safety program to improve diagnostic processes in medical inpatients. J Hosp Med 2023; 18 (12) 1072-1081
    CrossrefPubMedGoogle Scholar
  • 21 Bourgeois FC, Hart NJ, Dong Z. et al. Partnering with patients and families to improve diagnostic safety through the OurDX tool: effects of race, ethnicity, and language preference. Appl Clin Inform 2023; 14 (05) 903-912
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Malik MA, Motta-Calderon D, Piniella N. et al. A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. Diagnosis (Berl) 2022; 9 (04) 446-457
    CrossrefPubMedGoogle Scholar
  • 23 Garber A, Malik M, Bersani K. et al. Improving patient-provider communication about diagnoses in the acute care setting: an EHR-integrated patient questionnaire. J Hosp Med 2020 ; SHM Converge: Abstract 387
    PubMedGoogle Scholar
  • 24 Garber ACK, Malik M, Wu L. et al. Refining requirements for an electronic health record (EHR)-integrated intervention to improve diagnostic safety in acute care: a user-centered approach. JAMIA Open 2023; 6 (02) ooad031
    CrossrefPubMedGoogle Scholar
  • 25 Mlaver E, Schnipper JL, Boxer RB. et al. User-centered collaborative design and development of an inpatient safety dashboard. Jt Comm J Qual Patient Saf 2017; 43 (12) 676-685
    PubMedGoogle Scholar
  • 26 Fuller TE, Pong DD, Piniella N. et al. Interactive digital health tools to engage patients and caregivers in discharge preparation: implementation study. J Med Internet Res 2020; 22 (04) e15573
    CrossrefPubMedGoogle Scholar
  • 27 Fuller TE, Garabedian PM, Lemonias DP. et al. Assessing the cognitive and work load of an inpatient safety dashboard in the context of opioid management. Appl Ergon 2020; 85: 103047
    CrossrefPubMedGoogle Scholar
  • 28 Dalal AK, Piniella N, Fuller TE. et al. Evaluation of electronic health record-integrated digital health tools to engage hospitalized patients in discharge preparation. J Am Med Inform Assoc 2021; 28 (04) 704-712
    CrossrefPubMedGoogle Scholar
  • 29 Bersani KFT, Garabedian P, Espares J. et al. Use, perceived usability, and barriers to implementation of a patient safety dashboard integrated within a vendor EHR. Appl Clin Inform 2020; 11 (01) 34-45
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Dalal AK, Dykes P, Samal L. et al. Potential of an electronic health record-integrated patient portal for improving care plan concordance during acute care. Appl Clin Inform 2019; 10 (03) 358-366
    Article in Thieme ConnectPubMedGoogle Scholar
  • 31 Marshall TL, Hagedorn PA, Sump C. et al. Diagnosis code and health care utilization patterns associated with diagnostic uncertainty. Hosp Pediatr 2022; 12 (12) 1066-1072
    CrossrefPubMedGoogle Scholar
  • 32 Marshall TL, Nickels LC, Brady PW, Edgerton EJ, Lee JJ, Hagedorn PA. Developing a machine learning model to detect diagnostic uncertainty in clinical documentation. J Hosp Med 2023; 18 (05) 405-412
    CrossrefPubMedGoogle Scholar
  • 33 Bhise V, Sittig DF, Vaghani V, Wei L, Baldwin J, Singh H. An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients. BMJ Qual Saf 2018; 27 (03) 241-246
    CrossrefPubMedGoogle Scholar
  • 34 Steitz BD, McCoy AB, Reese TJ. et al. Development and validation of a machine learning algorithm using clinical pages to predict imminent clinical deterioration. J Gen Intern Med 2024; 39 (01) 27-35
    CrossrefPubMedGoogle Scholar
  • 35 Lyles CR, Wachter RM, Sarkar U. Focusing on digital health equity. JAMA 2021; 326 (18) 1795-1796
    CrossrefPubMedGoogle Scholar
  • 36 Adedinsewo D, Eberly L, Sokumbi O, Rodriguez JA, Patten CA, Brewer LC. Health disparities, clinical trials, and the digital divide. Mayo Clin Proc 2023; 98 (12) 1875-1887
    CrossrefPubMedGoogle Scholar
  • 37 Diamond JE, Kaltenbach LA, Granger BB. et al. Access to mobile health interventions among patients hospitalized with heart failure: insights into the digital divide from the CONNECT-HF mHealth substudy. Circ Heart Fail 2024; 17 (02) e011140
    PubMedGoogle Scholar
  • 38 Rodriguez JA, Charles JP, Bates DW, Lyles C, Southworth B, Samal L. Digital healthcare equity in primary care: implementing an integrated digital health navigator. J Am Med Inform Assoc 2023; 30 (05) 965-970
    CrossrefPubMedGoogle Scholar
  • 39 Plombon S, SRudin R, Sulca Flores J. et al. Assessing equitable recruitment in a digital health trial for asthma. Appl Clin Inform 2023; 14 (04) 620-631
    Article in Thieme ConnectPubMedGoogle Scholar
  • 40 Meyer AND, Giardina TD, Khawaja L, Singh H. Patient and clinician experiences of uncertainty in the diagnostic process: current understanding and future directions. Patient Educ Couns 2021; 104 (11) 2606-2615
    CrossrefPubMedGoogle Scholar
  • 41 Rao G, Kirley K, Epner P. et al. Identifying, analyzing, and visualizing diagnostic paths for patients with nonspecific abdominal pain. Appl Clin Inform 2018; 9 (04) 905-913
    Article in Thieme ConnectPubMedGoogle Scholar
  • 42 Kripalani S, Bengtzen R, Henderson LE, Jacobson TA. Clinical research in low-literacy populations: using teach-back to assess comprehension of informed consent and privacy information. IRB 2008; 30 (02) 13-19
    PubMedGoogle Scholar
  • 43 McDonald KM. Achieving equity in diagnostic excellence. JAMA 2022; 327 (20) 1955-1956
    CrossrefPubMedGoogle Scholar
  • 44 López L, Rodriguez F, Huerta D, Soukup J, Hicks L. Use of interpreters by physicians for hospitalized limited English proficient patients and its impact on patient outcomes. J Gen Intern Med 2015; 30 (06) 783-789
    CrossrefPubMedGoogle Scholar