Appl Clin Inform 2014; 05(04): 1015-1025
DOI: 10.4338/ACI-2014-05-RA-0048
Research Article
Schattauer GmbH

User Centered Clinical Decision Support Tools

Adoption across Clinician Training Level
L.J. McCullagh
1   Department of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY
,
A. Sofianou
2   Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine, NYC, NY
,
J. Kannry
2   Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine, NYC, NY
,
D.M. Mann
3   Department of Medicine, Section of Preventive Medicine & Epidemiology, Boston University School of Medicine, Boston, MA
,
T.G. McGinn
1   Department of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY
› Author Affiliations
Further Information

Publication History

received: 13 May 2014

accepted: 13 September 2014

Publication Date:
19 December 2017 (online)

Summary

Background: Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act’s Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that ‘early adopters’ are more inclined to use CDS tools and younger physicians tend to be ranked in this category.

Objective: This study examined the differences in adoption of CDS tools across providers’ training level.

Participants: From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial.

Intervention: The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits.

Main Measures: The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings.

Key Results: The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents.

Conclusion: There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians’ training levels.

Citation: McCullagh LJ, Sofianou A, Kannry J, Mann DM, McGinn TG. User centered clinical decision support tools: Adoption across clinician training level. Appl Clin Inf 2014; 5: 1015–1025

http://dx.doi.org/10.4338/ACI-2014-05-RA-0048

 
  • References

  • 1 King J, Patel V, Furukawa M. Physician Adoption of Electronic Health Record Technology to Meet Meaningful Use Objectives: 2009–2012. Washington, DC: Office of the National Coordinator for Health Information Technology.; 2012
  • 2 Charles D, King J, Furukawa M, Patel V. Hospital Adoption of Electronic Health Record Technology to Meet Meaningful Use Objectives: 2008–2012. Washington, DC: Office of the National Coordinator for Health Information Technology,; 2013
  • 3 ONC. ONC analysis of data from the 2011 American Hospital Association Survey Information Technolog Supplement. 2011
  • 4 McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 2000; 284 (01) 79-84.
  • 5 Linder JA, Schnipper JL, Tsurikova R, Yu DT, Volk LA, Melnikas AJ. et al. Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections. Am J Manag Care 2010; 16 12 Suppl. HIT e311-e319.
  • 6 CMS. EHR Incentive Program: Data and Reports 2012 [cited 2012 July 27]. Available from: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/DataAndReports.html.
  • 7 Caldis T. Composite health plan quality scales. Health Care Financ Rev 2007; 28 (03) 95-107.
  • 8 Center for Medicare and Medicaid Services (CMS). Federal Register | Electronic Health Record (EHR) Incentive Program (CMS-0033-F) [cited 2010 Sept. 5]. Available from: http://www.federalregister.gov/regulations/0938-AP78/electronic-health-record-ehr-incentive-program-cms-0033-f-.
  • 9 Medicare and Medicaid Programs; Electronic Health Record Incentive Program - Stage 2. Proposed Rule. Fed Regist 2012; 77 (45) 13698-13827.
  • 10 Eslami S, Abu-Hanna A, Schultz MJ, de Jonge E, de Keizer NF. Evaluation of consulting and critiquing decision support systems: Effect on adherence to a lower tidal volume mechanical ventilation strategy. J Crit Care. 2011
  • 11 Lo HG, Newmark LP, Yoon C, Volk LA, Carlson VL, Kittler AF. et al. Electronic health records in specialty care: a time-motion study. J Am Med Inform Assoc 2007; 14 (05) 609-615.
  • 12 Welch WP, Bazarko D, Ritten K, Burgess Y, Harmon R, Sandy LG. Electronic health records in four community physician practices: impact on quality and cost of care. J Am Med Inform Assoc 2007; 14 (03) 320-328.
  • 13 Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. Journal of the American Medical Informatics Association 2011; 18 (03) 327-334.
  • 14 Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011; 171 (10) 897-903.
  • 15 Bernstein SL, Whitaker D, Winograd J, Brennan JA. An electronic chart prompt to decrease proprietary antibiotic prescription to self-pay patients. Acad Emerg Med 2005; 12 (03) 225-231.
  • 16 Garthwaite EA, Will EJ, Bartlett C, Richardson D, Newstead CG. Patient-specific prompts in the cholesterol management of renal transplant outpatients: results and analysis of underperformance. Transplantation 2004; 78 (07) 1042-1047.
  • 17 Safran C, Rind DM, Davis RM, Currier J, Ives D, Sands DZ. et al. An electronic medical record that helps care for patients with HIV infection. Proc Annu Symp Comput Appl Med Care 1993: 224-228.
  • 18 Safran C, Rind DM, Davis RB, Ives D, Sands DZ, Currier J. et al. Guidelines for management of HIV infection with computer-based patient’s record. Lancet 1995; 346 8971 341-346.
  • 19 Safran C, Rind DM, Sands DZ, Davis RB, Wald J, Slack WV. Development of a knowledge-based electronic patient record. MD Comput 1996; 13 (01) 46-54 63.
  • 20 Tierney WM, Miller ME, McDonald CJ. The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests [see comments]. N Engl J Med 1990; 322 (21) 1499-1504.
  • 21 Simon SR, Smith DH, Feldstein AC, Perrin N, Yang X, Zhou Y. et al. Computerized prescribing alerts and group academic detailing to reduce the use of potentially inappropriate medications in older people. J Am Geriatr Soc 2006; 54 (06) 963-968.
  • 22 Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B. et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 2006; 13 (01) 5-11.
  • 23 Tamblyn R, Huang A, Perreault R, Jacques A, Roy D, Hanley J. et al. The medical office of the 21st century (MOXXI): effectiveness of computerized decision-making support in reducing inappropriate prescribing in primary care. CMAJ 2003; 169 (06) 549-556.
  • 24 Tamblyn R, Huang A, Kawasumi Y, Bartlett G, Grad R, Jacques A. et al. The development and evaluation of an integrated electronic prescribing and drug management system for primary care. J Am Med Inform Assoc 2006; 13 (02) 148-159.
  • 25 Gaikwad R, Sketris I, Shepherd M, Duffy J. Evaluation of accuracy of drug interaction alerts triggered by two electronic medical record systems in primary healthcare. Health Informatics J 2007; 13 (03) 163-177.
  • 26 Smith DH, Perrin N, Feldstein A, Yang X, Kuang D, Simon SR. et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record: an interrupted time series evaluation. Arch Intern Med 2006; 166 (Suppl. 10) 1098-1104.
  • 27 Seidling HM, Schmitt SP, Bruckner T, Kaltschmidt J, Pruszydlo MG, Senger C. et al. Patient-specific electronic decision support reduces prescription of excessive doses. Qual Saf Health Care 2010; 19: e15.
  • 28 Hemens BJ, Holbrook A, Tonkin M, Mackay JA, Weise-Kelly L, Navarro T. et al. Computerized clinical decision support systems for drug prescribing and management: a decision-maker-researcher partnership systematic review. Implement Sci 2011; 6: 89.
  • 29 Eccles M, McColl E, Steen N, Rousseau N, Grimshaw J, Parkin D. et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. BMJ 2002; 325 7370 941.
  • 30 Cresswell K, Majeed A, Bates DW, Sheikh A. Computerised decision support systems for healthcare professionals: an interpretative review. Inform Prim Care 2012; 20 (02) 115-128.
  • 31 Eslami S, Abu-Hanna A, de Keizer NF. Evaluation of Outpatient Computerized Physician Medication Order Entry Systems: A Systematic Review. J Am Med Inform Assoc 2007; 14 (04) 400-406.
  • 32 Steele AW, Eisert S, Witter J, Lyons P, Jones MA, Gabow P. et al. The effect of automated alerts on provider ordering behavior in an outpatient setting. PLoS Med 2005; 2 (09) e255.
  • 33 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238.
  • 34 Krall MA, Sittig DF. Clinician’s assessments of outpatient electronic medical record alert and reminder usability and usefulness requirements. Proc AMIA Symp 2002: 400-404.
  • 35 Sequist TD, Morong SM, Marston A, Keohane CA, Cook EF, Orav EJ. et al. Electronic risk alerts to improve primary care management of chest pain: a randomized, controlled trial. J Gen Intern Med 2012; 27 (04) 438-444.
  • 36 Jones JB, Stewart WF, Darer JD, Sittig DF. Beyond the threshold: real-time use of evidence in practice. BMC Med Inform Decis Mak 2013; 13: 47.
  • 37 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (03) i68-i74.
  • 38 Capra F. The hidden connections. New York: Anchor Books; 2002
  • 39 Pettigrew AM, Ferlie E, and McKee L. Shaping Strategic Change: Making Change in Large Organisations –The case of the National Health Service. London: Sage Publications,; 1992
  • 40 Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR. et al. Effect of Clinical Decision-Support Systems: A Systematic Review. Ann Intern Med. 2012
  • 41 P. P. Complexity and the adoption of innovation in health care. Accelerating Quality Improvement in Health Care: Strategies to Accelerate the Diffusion of Evidence-Based InnovationsNational Institute for Health Care Management Foundation and National Committee for Quality in Health Care. Washington, DC: 2003
  • 42 Saleem JJ, Patterson ES, Militello L, Render ML, Orshansky G, Asch SM. Exploring barriers and facilitators to the use of computerized clinical reminders. J Am Med Inform Assoc 2005; 12 (04) 438-447.
  • 43 Saleem JJ, Patterson ES, Militello L, Anders S, Falciglia M, Wissman JA. et al. Impact of clinical reminder redesign on learnability, efficiency, usability, and workload for ambulatory clinic nurses. J Am Med Inform Assoc 2007; 14 (05) 632-640.
  • 44 Zheng K, Padman R, Johnson MP, Diamond HS. Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system. Int J Med Inform 2005; 74 7–8 535-543.
  • 45 Horsky J, Schiff GD, Johnston D, Mercincavage L, Bell D, Middleton B. Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions. J Biomed Inform 2012; 45 (06) 1202-1216.
  • 46 Geibert RC. Using diffusion of innovation concepts to enhance implementation of an electronic health record to support evidence-based practice. Nurs Adm Q 2006; 30 (03) 203-210.
  • 47 Horbar JD, Rogowski J, Plsek PE, Delmore P, Edwards WH, Hocker J. et al. Collaborative quality improvement for neonatal intensive care. NIC/Q Project Investigators of the Vermont Oxford Network. Pediatrics 2001; 107 (01) 14-22.
  • 48 Plsek P. Innovative thinking for the improvement of medical systems. Ann Intern Med 1999; 131 (06) 438-444.
  • 49 Wu HW, Davis PK, Bell DS. Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review. BMC Med Inform Decis Mak 2012; 12: 90.
  • 50 Li AC, Kannry JL, Kushniruk A, Chrimes D, McGinn TG, Edonyabo D. et al. Integrating usability testing and think-aloud protocol analysis with „near-live“ clinical simulations in evaluating clinical decision support. Int J Med Inform. 2012
  • 51 McGinn TG, McCullagh L, Kannry J, Knaus M, Sofianou A, Wisnivesky JP. et al. Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Intern Med 2013; 173 (17) 1584-1591. doi: 10.1001/jamainternmed.2013.8980.
  • 52 Centor RM, Witherspoon JM, Dalton HP, Brody CE, Link K. The diagnosis of strep throat in adults in the emergency room. Med Decis Making 1981; 1 (03) 239-246.
  • 53 Walsh BT, Bookheim WW, Johnson RC, Tompkins RK. Recognition of streptococcal pharyngitis in adults. Arch Intern Med 1975; 135 (11) 1493-1497.
  • 54 McGinn TG, Deluca J, Ahlawat SK, Mobo Jr. BH, Wisnivesky JP. Validation and modification of streptococcal pharyngitis clinical prediction rules. Mayo Clin Proc 2003; 78 (03) 289-293.
  • 55 Heckerling PS, Tape TG, Wigton RS, Hissong KK, Leikin JB, Ornato JP. et al. Clinical prediction rule for pulmonary infiltrates. Ann Intern Med 1990; 113 (09) 664-670.
  • 56 Mann DM, Kannry JL, Edonyabo D, Li AC, Arciniega J, Stulman J. et al. Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR) randomized trial in primary care. Implement Sci 2011; 6 (01) 109.
  • 57 Wu RC, Abrams H, Baker M, Rossos PG. Implementation of a computerized physician order entry system of medications at the University Health Network--physicians’ perspectives on the critical issues. Healthc Q 2006; 9 (01) 106-109.
  • 58 Wilkinson SA, Hinchliffe F, Hough J, Chang A. Baseline evidence-based practice use, knowledge, and attitudes of allied health professionals: a survey to inform staff training and organisational change. J Allied Health 2012; 41 (04) 177-184.