The Use of Evidence-Based, Problem-Oriented Templates as a Clinical Decision Support in an Inpatient Electronic Health Record SystemFunding Source This work was supported by the Gatorade Trust through funds distributed by the University of Florida, Department of Medicine.
02 March 2016
accepted: 30 May 2016
19 December 2017 (online)
The integration of clinical decision support (CDS) in documentation practices remains limited due to obstacles in provider workflows and design restrictions in electronic health records (EHRs). The use of electronic problem-oriented templates (POTs) as a CDS has been previously discussed but not widely studied.
We evaluated the voluntary use of evidence-based POTs as a CDS on documentation practices.
This was a randomized cohort (before and after) study of Hospitalist Attendings in an Academic Medical Center using EPIC EHRs. Primary Outcome measurement was note quality, assessed by the 9-item Physician Documentation Quality Instrument (PDQI-9). Secondary Outcome measurement was physician efficiency, assessed by the total charting time per note.
Use of POTs increased the quality of note documentation [score 37.5 vs. 39.0, P = 0.0020]. The benefits of POTs scaled with use; the greatest improvement in note quality was found in notes using three or more POTs [score 40.2, P = 0.0262]. There was no significant difference in total charting time [30 minutes vs. 27 minutes, P = 0.42].
Use of evidence-based and problem-oriented templates is associated with improved note quality without significant change in total charting time. It can be used as an effective CDS during note documentation.
Citation: Mehta R, Radhakrishnan NS, Warring CD, Jain A, Fuentes J, Dolganiuc A, Lourdes LS, Busigin J, Leverence RR. The use of evidence-based, problemoriented templates as a clinical decision support in an inpatient electronic health record system.
- 1 Shoolin J, Ozeran L, Hamann C, Bria II W. Association of Medical Directors of Information Systems Consensus on Inpatient Electronic Health Record Documentation. Appl Clin Inform 2013; 04 (02) 293-303.
- 2 Siegler EL. The Evolving Medical Record. Ann Intern Med 2010; 153 (10) 671-677.
- 3 Edwards ST, Neri PM, Volk LA, Schiff GD, Bates DW. Association of note quality and quality of care: a cross-sectional study. BMJ Qual Saf 2014; 23 (05) 406-413.
- 4 Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. J Am Med Inform Assoc 2010; 17 (01) 49-53.
- 5 Weed LL. Medical records that guide and teach. N Engl J Med 1968; 278 (12) 652-657.
- 6 Jacobs L. Interview with Lawrence Weed, MD - The Father of the Problem-Oriented Medical Record Looks Ahead. Perm J 2009; 13 (03) 84-89.
- 7 Dawes KS. General Practice Observed: Survey of General Practice Records. BMJ 1972; 03 (5820): 219-223.
- 8 Kargul GJ, Wright SM, Knight AM, McNichol MT, Riggio JM. The hybrid progress note: semiautomating daily progress notes to achieve high-quality documentation and improve provider efficiency. Am J Med Qual Off J Am Coll Med Qual 2013; 28 (01) 25-32.
- 9 Haynes RB, Devereaux PJ, Guyatt GH. Clinical expertise in the era of evidence-based medicine and patient choice. Evid Based Med 2002; 07 (02) 36-38.
- 10 Neri PM, Volk LA, Samaha S, Pollard SE, Williams DH, Fiskio JM, Burdick E, Edwards ST, Ramelson H, Schiff GD, Bates DW. Relationship between documentation method and quality of chronic disease visit notes. Appl Clin Inform 2014; 05 (02) 480-490.
- 11 Horowitz N, Moshkowitz M, Leshno M, Ribak J, Birkenfeld S, Kenet G, Halpern Z. Clinical trial: evaluation of a clinical decision-support model for upper abdominal complaints in primary-care practice. Aliment Pharmacol Ther 2007; 26 (09) 1277-1283.
- 12 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392.
- 13 Sweidan M, Reeve J, Dartnell J, Phillips S. Improving clinical decision support tools - challenges and a way forward. Aust Fam Physician 2011; 40 (08) 561-562.
- 14 Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, Middleton B, Bates DW. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc JAMIA 2013; 20 (03) 489-493.
- 15 McGowan JJ, Winstead-Fry P. Problem Knowledge Couplers: reengineering evidence-based medicine through interdisciplinary development, decision support, and research. Bull Med Libr Assoc 1999; 87 (04) 462-470.
- 16 Schnipper JL, Linder JA, Palchuk MB, Einbinder JS, Li Q, Postilnik A, Middleton B. “Smart Forms” in an Electronic Medical Record: Documentation-based Clinical Decision Support to Improve Disease Management. J Am Med Inform Assoc 2008; 15 (04) 513-523.
- 17 Doan S, Bastarache L, Klimkowski S, Denny JC, Xu H. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inform Assoc JAMIA 2010; 17 (05) 528-531.
- 18 Mehrotra A, Dellon ES, Schoen RE, Saul M, Bishehsari F, Farmer C, Harkema H. Applying a natural language processing tool to electronic health records to assess performance on colonoscopy quality measures. Gastrointest Endosc 2012; 75 (06) 1233-9 e14.
- 19 Meystre SM, Haug PJ. Comparing natural language processing tools to extract medical problems from narrative text. AMIA Annu Symp Proc AMIA Symp AMIA Symp 2005; 525-529.
- 20 Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc JAMIA 2011; 18 (05) 544-551.
- 21 Spasic I, Sarafraz F, Keane JA, Nenadic G. Medication information extraction with linguistic pattern matching and semantic rules. J Am Med Inform Assoc JAMIA 2010; 17 (05) 532-535.
- 22 Stetson PD, Bakken S, Wrenn JO, Siegler EL. Assessing Electronic Note Quality Using the Physician Documentation Quality Instrument (PDQI-9). Appl Clin Inform 2012; 03 (02) 164-174.
- 23 Fabri PJ, Knierim TH. Simple calculation of the unpaired t test. Surg Gynecol Obstet 1988; 167 (05) 381-382.
- 24 Kardys I, Hoeks S, van Domburg R, Lenzen M, Boersma E. Tools and techniques - statistics: analysis of continuous data using the t-test and ANOVA. EuroIntervention J Eur Collab Work Group Interv Cardiol Eur Soc Cardiol 2013; 09 (06) 765-767.
- 25 Rich JT, Neely JG, Paniello RC, Voelker CCJ, Nussenbaum B, Wang EW. A practical guide to understanding Kaplan-Meier curves. Otolaryngol--Head Neck Surg Off J Am Acad Otolaryngol-Head Neck Surg 2010; 143 (03) 331-336.
- 26 Reynolds M, Hickson M, Jacklin A, Franklin BD. A descriptive exploratory study of how admissions caused by medication-related harm are documented within inpatients’ medical records. BMC Health Serv Res 2014; 14: 257.
- 27 Talmon G, Horn A, Wedel W, Miller R, Stefonek A, Rinehart T. How well do we communicate?.: a comparison of intraoperative diagnoses listed in pathology reports and operative notes. Am J Clin Pathol 2013; 140 (05) 651-657.
- 28 Bernat JL. Ethical and quality pitfalls in electronic health records. Neurology 2013; 80 (11) 1057-1061.
- 29 Peabody JW, Luck J, Glassman P, Dresselhaus TR, Lee M. Comparison of vignettes, standardized patients, and chart abstraction: A prospective validation study of 3 methods for measuring quality. JAMA 2000; 283 (13) 1715-1722.
- 30 Lowe JR, Raugi GJ, Reiber GE, Whitney JD. Does incorporation of a clinical support template in the electronic medical record improve capture of wound care data in a cohort of veterans with diabetic foot ulcers?. J Wound Ostomy Cont Nurs Off Publ Wound Ostomy Cont Nurses Soc WOCN 2013; 40 (02) 157-162.
- 31 Beck AF, Sauers HS, Kahn RS, Yau C, Weiser J, Simmons JM. Improved documentation and care planning with an asthma-specific history and physical. Hosp Pediatr 2012; 02 (04) 194-201.
- 32 Ghani Y, Thakrar R, Kosuge D, Bates P. “Smart” electronic operation notes in surgery: an innovative way to improve patient care. Int J Surg Lond Engl 2014; 12 (01) 30-32.
- 33 Nguyen MC, Richardson DM, Hardy SG, Cookson RM, Mackenzie RS, Greenberg MR, Glenn-Porter B, Kane BG. Computer-based reminder system effectively impacts physician documentation. Am J Emerg Med 2014; 32 (01) 104-106.
- 34 Woller SC, Stevens SM, Towner S, Olson J, Christensen P, Hamilton S, Newman L, Mott L, Hu P, Brunisholz KD, Long Y, Lloyd J, Evans RS, Cannon W, Elliott CG. Computerized Clinical Decision Support Improves Warfarin Management and Decreases Recurrent Venous Thromboembolism. Clin Appl Thromb Hemost 2015; 21 (03) 197-203.
- 35 Groshaus H, Boscan A, Khandwala F, Holroyd-Leduc J. Use of clinical decision support to improve the quality of care provided to older hospitalized patients. Appl Clin Inform 2012; 03 (01) 94-102.
- 36 Riggio JM, Sorokin R, Moxey ED, Mather P, Gould S, Kane GC. Effectiveness of a clinical-decision-support system in improving compliance with cardiac-care quality measures and supporting resident training. Acad Med J Assoc Am Med Coll 2009; 84 (12) 1719-1726.
- 37 Roth CP, Lim Y-W, Pevnick JM, Asch SM, McGlynn EA. The Challenge of Measuring Quality of Care From the Electronic Health Record. Am J Med Qual 2009; 24 (05) 385-394.
- 38 Kern LM, Malhotra S, Barrón Y, Quaresimo J, Dhopeshwarkar R, Pichardo M, Edwards AM, Kaushal R. Accuracy of Electronically Reported “Meaningful Use” Clinical Quality MeasuresA Cross-sectional Study. Ann Intern Med 2013; 158 (02) 77-83.