Factors Influencing Problem List Use in Electronic Health Records—Application of the Unified Theory of Acceptance and Use of TechnologyFunding This study was funded by Academisch Medisch Centrum 2019-AMC-JK-7.
20 February 2020
22 April 2020
10 June 2020 (online)
Background Problem-oriented electronic health record (EHR) systems can help physicians to track a patient's status and progress, and organize clinical documentation, which could help improving quality of clinical data and enable data reuse. The problem list is central in a problem-oriented medical record. However, current problem lists remain incomplete because of the lack of end-user training and inaccurate content of underlying terminologies. This leads to modifications of diagnosis code descriptions and use of free-text notes, limiting reuse of data.
Objectives We aimed to investigate factors that influence acceptance and actual use of the problem list, and used these to propose recommendations, to increase the value of problem lists for (re)use.
Methods Semistructured interviews were conducted with physicians, heads of medical departments, and data quality experts, who were invited through snowball sampling. The interviews were transcribed and coded. Comments were fitted in constructs of the validated framework unified theory of acceptance user technology (UTAUT), and were discussed in terms of facilitators and barriers.
Results In total, 24 interviews were conducted. We found large variability in attitudes toward problem list use. Barriers included uncertainty about the responsibility for maintaining the problem list and little perceived benefits. Facilitators included the (re)design of policies, improved (peer-to-peer) training to increase motivation, and positive peer feedback and monitoring. Motivation is best increased through sharing benefits relevant in the care process, such as providing overview, timely generation of discharge or referral letters, and reuse of data. Furthermore, content of the underlying terminology should be improved and the problem list should be better presented in the EHR system.
Conclusion To let physicians accept and use the problem list, policies and guidelines should be redesigned, and prioritized by supervising staff. Additionally, peer-to-peer training on the benefits of using the problem list is needed.
Keywordsproblem-oriented medical records - facilitators and barriers - electronic health records and systems - secondary use - adoption - qualitative
Protection of Human and Animal Subjects
The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed and approved by the Medical Ethical Testing Committee (METC) of Amsterdam UMC.
- 1 Simons SM, Cillessen FH, Hazelzet JA. Determinants of a successful problem list to support the implementation of the problem-oriented medical record according to recent literature. BMC Med Inform Decis Mak 2016; 16 (102) 102
- 2 Salmon P, Rappaport A, Bainbridge M, Hayes G, Williams J. Taking the problem oriented medical record forward. Proc AMIA Annu Fall Symp 1996; 463-467
- 3 Hartung DM, Hunt J, Siemienczuk J, Miller H, Touchette DR. Clinical implications of an accurate problem list on heart failure treatment. J Gen Intern Med 2005; 20 (02) 143-147
- 4 Buchanan J. Accelerating the benefits of the problem oriented medical record. Appl Clin Inform 2017; 8 (01) 180-190
- 5 Wright A, Maloney FL, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11 (36) 36
- 6 Simborg DW, Starfield BH, Horn SD, Yourtee SA. Information factors affecting problem follow-up in ambulatory care. Med Care 1976; 14 (10) 848-856
- 7 Hose B-Z, Hoonakker PLT, Wooldridge AR. , et al. Physician perceptions of the electronic problem list in pediatric trauma care. Appl Clin Inform 2019; 10 (01) 113-122
- 8 Wright A, Feblowitz J, Maloney FL. , et al. Increasing patient engagement: patients' responses to viewing problem lists online. Appl Clin Inform 2014; 5 (04) 930-942
- 9 Holmes C, Brown M, Hilaire DS, Wright A. Healthcare provider attitudes towards the problem list in an electronic health record: a mixed-methods qualitative study. BMC Med Inform Decis Mak 2012; 12 (127) 127
- 10 Wright A, McCoy AB, Hickman T-TT. , et al. Problem list completeness in electronic health records: a multi-site study and assessment of success factors. Int J Med Inform 2015; 84 (10) 784-790
- 11 Walji MF, Kalenderian E, Tran D. , et al. Detection and characterization of usability problems in structured data entry interfaces in dentistry. Int J Med Inform 2013; 82 (02) 128-138
- 12 Andrews JC, Bogliatto F, Lawson HW, Bornstein J. Speaking the same language: using standardized terminology. J Low Genit Tract Dis 2016; 20 (01) 8-10
- 13 Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. J Am Med Inform Assoc 2006; 13 (03) 277-288
- 14 Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. A model for evaluating interface terminologies. J Am Med Inform Assoc 2008; 15 (01) 65-76
- 15 Horsky J, Drucker EA, Ramelson HZ. Accuracy and completeness of clinical coding using ICD-10 for ambulatory visits. AMIA Annu Symp Proc 2018; 2017: 912-920
- 16 Kim S, Lee K-H, Hwang H, Yoo S. Analysis of the factors influencing healthcare professionals' adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Med Inform Decis Mak 2016; 16 (01) 12
- 17 Ajami S, Bagheri-Tadi T. Barriers for adopting electronic health records (EHRs) by physicians. Acta Inform Med 2013; 21 (02) 129-134
- 18 Abbott PA, Foster J, Marin HdeF, Dykes PC. Complexity and the science of implementation in health IT--knowledge gaps and future visions. Int J Med Inform 2014; 83 (07) e12-e22
- 19 McGinn CA, Grenier S, Duplantie J. , et al. Comparison of user groups' perspectives of barriers and facilitators to implementing electronic health records: a systematic review. BMC Med 2011; 9 (46) 46
- 20 Vishwanath A, Scamurra SD. Barriers to the adoption of electronic health records: using concept mapping to develop a comprehensive empirical model. Health Informatics J 2007; 13 (02) 119-134
- 21 Martin PM, Sbaffi L. Electronic health record and problem lists in Leeds, United Kingdom: variability of general practitioners' views. Health Informatics J 2019; 0 (00) 1460458219895184
- 22 Hennington A, Janz BD. Information systems and healthcare XVI: physician adoption of electronic medical records: applying the UTAUT model in a healthcare context. Comm Assoc Inform Syst 2007; 19: 60-80
- 23 Ifinedo P. Technology acceptance by health professionals in Canada: an analysis with a modified UTAUT model. 2012 45th HICSS; 2012
- 24 Wills MJ, El-Gayar OF, Bennett D. Examining healthcare professionals' acceptance of electronic medical records using UTAUT. Issues Inf Syst 2008; 9 (02) 396-401
- 25 Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. Manage Inf Syst Q 2003; 425-478
- 26 Palau-Saumell R, Forgas-Coll S, Sánchez-García J, Robres E. User acceptance of mobile apps for restaurants: an expanded and extended UTAUT-2. Sustainability 2019; 11 (1210): 1-24
- 27 Dutch Hospital Data. Diagnosethesaurus. 2020 . Available at: https://www.dhd.nl/producten-diensten/diagnosethesaurus/Paginas/Diagnosethesaurus.aspx . Accessed March 23, 2020
- 28 Griffee DT. Research tips: interview data collection. J Dev Educ 2005; 28 (03) 36-37
- 29 Walker D, Myrick F. Grounded theory: an exploration of process and procedure. Qual Health Res 2006; 16 (04) 547-559
- 30 Benmessaoud C, Kharrazi H, MacDorman KF. Facilitators and barriers to adopting robotic-assisted surgery: contextualizing the unified theory of acceptance and use of technology. PLoS One 2011; 6 (01) e16395
- 31 Malik Bader Alazzam A, Samad Hasan Basari ASS, Ibrahim YM, Ramli MR, Naim MH. Trust in stored data in EHRs acceptance of medical staff: using UTAUT2. Int J Appl Eng Res. 2016; 11 (04) 2737-2748
- 32 Anderson MO, Jackson SL, Oster NV. , et al. Patients typing their own visit agendas into an electronic medical record: pilot in a safety-net clinic. Ann Fam Med 2017; 15 (02) 158-161
- 33 Miech EJ, Rattray NA, Flanagan ME, Damschroder L, Schmid AA, Damush TM. Inside help: an integrative review of champions in healthcare-related implementation. SAGE Open Med 2018; 6: 2050312118773261
- 34 Chen E, Garcia-Webb M. An analysis of free-text alcohol use documentation in the electronic health record: early findings and implications. Appl Clin Inform 2014; 5 (02) 402-415
- 35 Holmes C. The problem list beyond meaningful use. Part I: the problems with problem lists. J AHIMA 2011; 82 (02) 30-33 , quiz 34
- 36 Kim MS, Clarke MA, Belden JL, Hinton E. Usability challenges and barriers in EHR training of primary care resident physicians. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management; Springer; 2014
- 37 Karsh B-T, Weinger MB, Abbott PA, Wears RL. Health information technology: fallacies and sober realities. J Am Med Inform Assoc 2010; 17 (06) 617-623
- 38 Kruse CS, Kristof C, Jones B, Mitchell E, Martinez A. Barriers to electronic health record adoption: a systematic literature review. J Med Syst 2016; 40 (12) 252
- 39 Carnes KM, de Riese CS, de Riese WT. A cost-benefit analysis of medical scribes and electronic medical record system in an academic urology clinic. Urol Pract 2015; 2 (03) 101-105
- 40 Varpio L, Ajjawi R, Monrouxe LV, O'Brien BC, Rees CE. Shedding the cobra effect: problematising thematic emergence, triangulation, saturation and member checking. Med Educ 2017; 51 (01) 40-50