Overcoming Decisional Gaps in High-Risk Prescribing by Junior Physicians Using Simulation-Based Training: Protocol for a Randomized Controlled Trial

Julie C Lauffenburger, Matthew F DiFrancesco, Renee A Barlev, Ted Robertson, Erin Kim, Maxwell D Coll, Nancy Haff, Constance P Fontanet, Kaitlin Hanken, Rebecca Oran, Jerry Avorn, Niteesh K Choudhry, Julie C Lauffenburger, Matthew F DiFrancesco, Renee A Barlev, Ted Robertson, Erin Kim, Maxwell D Coll, Nancy Haff, Constance P Fontanet, Kaitlin Hanken, Rebecca Oran, Jerry Avorn, Niteesh K Choudhry

Abstract

Background: Gaps between rational thought and actual decisions are increasingly recognized as a reason why people make suboptimal choices in states of heightened emotion, such as stress. These observations may help explain why high-risk medications continue to be prescribed to acutely ill hospitalized older adults despite widely accepted recommendations against these practices. Role playing and other efforts, such as simulation training, have demonstrated benefits to help people avoid decisional gaps but have not been tested to reduce overprescribing of high-risk medications.

Objective: This study aims to evaluate the impact of a simulation-based training program designed to address decisional gaps on prescribing of high-risk medications compared with control.

Methods: In this 2-arm pragmatic trial, we are randomizing at least 36 first-year medical resident physicians (ie, interns) who provide care on inpatient general medicine services at a large academic medical center to either intervention (simulation-based training) or control (online educational training). The intervention comprises a 40-minute immersive individual simulation training consisting of a reality-based patient care scenario in a simulated environment at the beginning of their inpatient service rotation. The simulation focuses on 3 types of high-risk medications, including benzodiazepines, antipsychotics, and sedative hypnotics (Z-drugs), in older adults, and is specifically designed to help the physicians identify their reactions and prescribing decisions in stressful situations that are common in the inpatient setting. The simulation scenario is followed by a semistructured debriefing with an expert facilitator. The trial's primary outcome is the number of medication doses for any of the high-risk medications prescribed by the interns to patients aged 65 years or older who were not taking one of the medications upon admission. Secondary outcomes include prescribing by all providers on the care team, being discharged on 1 of the medications, and prescribing of related medications (eg, melatonin, trazodone), or the medications of interest for the control intervention. These outcomes will be measured using electronic health record data.

Results: Recruitment of interns began on March 29, 2021. Recruitment for the trial ended in Q42021, with follow-up completed by Q12022.

Conclusions: This trial will evaluate the impact of a simulation-based training program designed using behavioral science principles on prescribing of high-risk medications by junior physicians. If the intervention is shown to be effective, this approach could potentially be reproducible by others and for a broader set of behaviors.

Trial registration: ClinicalTrials.gov NCT04668248; https://ichgcp.net/clinical-trials-registry/NCT04668248.

International registered report identifier (irrid): PRR1-10.2196/31464.

Keywords: antipsychotics; behavioral science; benzodiazepines; impact evaluation; pragmatic trial; prescribing.

Conflict of interest statement

Conflicts of Interest: NC is a consultant to and holds equity in RxAnte, unrelated to this work. He receives grant funding, payable to his institution, from Boehringer Ingelheim and Humana, also unrelated to this work. RB is now an employee at Vytalize Health. The other authors report no conflicts.

©Julie C Lauffenburger, Matthew F DiFrancesco, Renee A Barlev, Ted Robertson, Erin Kim, Maxwell D Coll, Nancy Haff, Constance P Fontanet, Kaitlin Hanken, Rebecca Oran, Jerry Avorn, Niteesh K Choudhry. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.04.2022.

Figures

Figure 1
Figure 1
Overall trial design.
Figure 2
Figure 2
Timeline of study procedures.

References

    1. Loewenstein G. Hot-cold empathy gaps and medical decision making. Health Psychol. 2005 Jul;24(4S):S49–56. doi: 10.1037/0278-6133.24.4.S49.2005-08085-008
    1. Sayette MA, Loewenstein G, Griffin KM, Black JJ. Exploring the cold-to-hot empathy gap in smokers. Psychol Sci. 2008 Sep;19(9):926–32. doi: 10.1111/j.1467-9280.2008.02178.x. PSCI2178
    1. Webster C. More on "fast" and "slow" thinking in diagnostic reasoning. Acad Med. 2015 Jan;90(1):3. doi: 10.1097/ACM.0000000000000555.00001888-201501000-00003
    1. Alsoufi B. Thinking, fast and slow. J Thorac Cardiovasc Surg. 2017 Mar;153(3):646–647. doi: 10.1016/j.jtcvs.2016.11.024. S0022-5223(16)31614-2
    1. Kang MJ, Camerer CF. fMRI evidence of a hot-cold empathy gap in hypothetical and real aversive choices. Front Neurosci. 2013;7:104. doi: 10.3389/fnins.2013.00104. doi: 10.3389/fnins.2013.00104.
    1. Croskerry P. ED cognition: any decision by anyone at any time. CJEM. 2014 Jan;16(1):13–9. doi: 10.2310/8000.2013.131053.
    1. Tay SW, Ryan P, Ryan CA. Systems 1 and 2 thinking processes and cognitive reflection testing in medical students. Can Med Educ J. 2016 Oct;7(2):e97–e103.
    1. Ward T, Garety PA. Fast and slow thinking in distressing delusions: A review of the literature and implications for targeted therapy. Schizophr Res. 2017 Sep 16;:80–87. doi: 10.1016/j.schres.2017.08.045. S0920-9964(17)30521-2
    1. Siebolds M. [Evidence-based medicine as a model of decision making in clinical practice] Z Arztl Fortbild Qualitatssich. 2003 Jul;97(4-5):257–62.
    1. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013 Oct;22 Suppl 2:ii58–ii64. doi: 10.1136/bmjqs-2012-001712. bmjqs-2012-001712
    1. Lewis PJ, Tully MP. Uncomfortable prescribing decisions in hospitals: the impact of teamwork. J R Soc Med. 2009 Dec;102(11):481–8. doi: 10.1258/jrsm.2009.090150. 102/11/481
    1. Hill KD, Wee R. Psychotropic drug-induced falls in older people: a review of interventions aimed at reducing the problem. Drugs Aging. 2012 Jan 01;29(1):15–30. doi: 10.2165/11598420-000000000-00000.2
    1. Jordan S, Gabe-Walters ME, Watkins A, Humphreys I, Newson L, Snelgrove S, Dennis MS. Nurse-Led Medicines' Monitoring for Patients with Dementia in Care Homes: A Pragmatic Cohort Stepped Wedge Cluster Randomised Trial. PLoS One. 2015;10(10):e0140203. doi: 10.1371/journal.pone.0140203. PONE-D-15-24629
    1. Craftman. Johnell K, Fastbom J, Westerbotn M, von Strauss E. Time trends in 20 years of medication use in older adults: Findings from three elderly cohorts in Stockholm, Sweden. Arch Gerontol Geriatr. 2016;63:28–35. doi: 10.1016/j.archger.2015.11.010.S0167-4943(15)30087-X
    1. By the 2019 American Geriatrics Society Beers Criteria® Update Expert Panel American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc. 2019 Apr;67(4):674–694. doi: 10.1111/jgs.15767.
    1. Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. J Am Med Inform Assoc. 2012 Jun;19(e1):e145–8. doi: 10.1136/amiajnl-2011-000743. amiajnl-2011-000743
    1. Payne TH, Hines LE, Chan RC, Hartman S, Kapusnik-Uner J, Russ AL, Chaffee BW, Hartman C, Tamis V, Galbreth B, Glassman PA, Phansalkar S, van DSH, Gephart SM, Mann G, Strasberg HR, Grizzle AJ, Brown M, Kuperman GJ, Steiner C, Sullins A, Ryan H, Wittie MA, Malone DC. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc. 2015 Nov;22(6):1243–50. doi: 10.1093/jamia/ocv011.ocv011
    1. Roumie CL, Elasy TA, Wallston KA, Pratt S, Greevy RA, Liu X, Alvarez V, Dittus RS, Speroff T. Clinical inertia: a common barrier to changing provider prescribing behavior. Jt Comm J Qual Patient Saf. 2007 May;33(5):277–85. doi: 10.1016/s1553-7250(07)33032-8.S1553-7250(07)33032-8
    1. Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ. Adoption of a clinical decision support system to promote judicious use of antibiotics for acute respiratory infections in primary care. Int J Med Inform. 2012 Aug;81(8):521–6. doi: 10.1016/j.ijmedinf.2012.03.002.S1386-5056(12)00057-3
    1. Markota M, Rummans TA, Bostwick JM, Lapid MI. Benzodiazepine Use in Older Adults: Dangers, Management, and Alternative Therapies. Mayo Clin Proc. 2016 Dec;91(11):1632–1639. doi: 10.1016/j.mayocp.2016.07.024.S0025-6196(16)30509-2
    1. Davey P, Marwick CA, Scott CL, Charani E, McNeil K, Brown E, Gould IM, Ramsay CR, Michie S. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane Database Syst Rev. 2017 Dec 09;2:CD003543. doi: 10.1002/14651858.CD003543.pub4.
    1. Sequist TD, Zaslavsky AM, Colditz GA, Ayanian JZ. Electronic patient messages to promote colorectal cancer screening: a randomized controlled trial. Arch Intern Med. 2011 Apr 11;171(7):636–41. doi: 10.1001/archinternmed.2010.467. archinternmed.2010.467
    1. Angner E, Loewenstein G. Behavioral economics. In: Mäki U, editor. Handbook of the Philosophy of Science: Philosophy of Economic. Amsterdam, The Netherlands: Elsevier; 2007. pp. 641–90.
    1. Datta S, Burns J, Maughan-Brown B, Darling M, Eyal K. Risking it all for love? Resetting beliefs about HIV risk among low-income South African teens. Journal of Economic Behavior & Organization. 2015 Oct;118:184–198. doi: 10.1016/j.jebo.2015.02.020.
    1. Schoenthaler A, Albright G, Hibbard J, Goldman R. Simulated Conversations With Virtual Humans to Improve Patient-Provider Communication and Reduce Unnecessary Prescriptions for Antibiotics: A Repeated Measure Pilot Study. JMIR Med Educ. 2017 Apr 19;3(1):e7. doi: 10.2196/mededu.6305. v3i1e7
    1. Armenia S, Thangamathesvaran L, Caine AD, King N, Kunac A, Merchant AM. The Role of High-Fidelity Team-Based Simulation in Acute Care Settings: A Systematic Review. Surg J (N Y) 2018 Jul;4(3):e136–e151. doi: 10.1055/s-0038-1667315. 1800004ra
    1. Dale-Tam J, Posner GD. Alice's Delirium: A Theatre-based Simulation Scenario for Nursing. Cureus. 2018 May 02;10(4):e2411. doi: 10.7759/cureus.2411.
    1. Molloy MA, Cary MP, Brennan-Cook J, Cantey DS, Tocchi C, Bailey DE, Oermann MH. Preparing Clinicians for Transitioning Patients Across Care Settings and Into the Home Through Simulation. Home Healthc Now. 2018;36(4):225–231. doi: 10.1097/NHH.0000000000000667. 01845097-201807000-00004
    1. Kannampallil TG, McNutt R, Falck S, Galanter WL, Patterson D, Darabi H, Sharabiani A, Schiff G, Odwazny R, Vaida AJ, Wilkie DJ, Lambert BL. Learning optimal opioid prescribing and monitoring: a simulation study of medical residents. JAMIA Open. 2018 Oct;1(2):246–254. doi: 10.1093/jamiaopen/ooy026. ooy026
    1. Müller E, Diesing A, Rosahl A, Scholl I, Härter M, Buchholz A. Evaluation of a shared decision-making communication skills training for physicians treating patients with asthma: a mixed methods study using simulated patients. BMC Health Serv Res. 2019 Aug 30;19(1):612. doi: 10.1186/s12913-019-4445-y. 10.1186/s12913-019-4445-y
    1. Peltan ID, Shiga T, Gordon JA, Currier PF. Simulation Improves Procedural Protocol Adherence During Central Venous Catheter Placement: A Randomized Controlled Trial. Simul Healthc. 2015 Oct;10(5):270–6. doi: 10.1097/SIH.0000000000000096.
    1. Lee W, Kim M, Kang Y, Lee Y, Kim SM, Lee J, Hyun S, Yu J, Park Y. Nursing and medical students' perceptions of an interprofessional simulation-based education: a qualitative descriptive study. Korean J Med Educ. 2020 Dec;32(4):317–327. doi: 10.3946/kjme.2020.179. doi: 10.3946/kjme.2020.179.kjme.2020.179
    1. Moll-Khosrawi P, Zöllner C, Cencin N, Schulte-Uentrop L. Flipped learning enhances non-technical skill performance in simulation-based education: a randomised controlled trial. BMC Med Educ. 2021 Jul 22;21(1):353. doi: 10.1186/s12909-021-02766-w. 10.1186/s12909-021-02766-w
    1. Ho M, Yu L, Lin P, Chang HR, Traynor V, Huang W, Montayre J, Chen K. Effects of a simulation-based education programme on delirium care for critical care nurses: A randomized controlled trial. J Adv Nurs. 2021 Aug;77(8):3483–3493. doi: 10.1111/jan.14938.
    1. McCormick WC. Revised AGS Choosing Wisely(®) list: changes to help guide older adult care conversations. J Gerontol Nurs. 2015 May;41(5):49–50. doi: 10.3928/00989134-20150402-01.
    1. Borowitz SM, Waggoner-Fountain LA, Bass EJ, Sledd RM. Adequacy of information transferred at resident sign-out (in-hospital handover of care): a prospective survey. Qual Saf Health Care. 2008 Mar;17(1):6–10. doi: 10.1136/qshc.2006.019273.17/1/6
    1. Hsu N, Huang C, Jerng J, Hsu C, Yang M, Chang R, Ko W, Yu C. Influence of patient and provider factors on the workload of on-call physicians: A general internal medicine cohort observational study. Medicine (Baltimore) 2016 Aug;95(35):e4719. doi: 10.1097/MD.0000000000004719. doi: 10.1097/MD.0000000000004719.00005792-201608300-00070
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN, REDCap Consortium The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019 Jul;95:103208. doi: 10.1016/j.jbi.2019.103208. S1532-0464(19)30126-1
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377–81. doi: 10.1016/j.jbi.2008.08.010. S1532-0464(08)00122-6
    1. Marteau TM, Bekker H. The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) Br J Clin Psychol. 1992 Sep;31 ( Pt 3):301–6.
    1. Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A) Arthritis Care Res (Hoboken) 2011 Nov;63 Suppl 11:S467–72. doi: 10.1002/acr.20561. doi: 10.1002/acr.20561.
    1. Gerrity MS, DeVellis RF, Earp JA. Physicians' reactions to uncertainty in patient care. A new measure and new insights. Med Care. 1990 Aug;28(8):724–36. doi: 10.1097/00005650-199008000-00005.
    1. Beutel ME, Brähler E, Ernst M, Klein E, Reiner I, Wiltink J, Michal M, Wild PS, Schulz A, Münzel T, Hahad O, König J, Lackner KJ, Pfeiffer N, Tibubos AN. Noise annoyance predicts symptoms of depression, anxiety and sleep disturbance 5 years later. Findings from the Gutenberg Health Study. Eur J Public Health. 2020 Jun 01;30(3):516–521. doi: 10.1093/eurpub/ckaa015.5731324
    1. Kamdar BB, Martin JL, Needham DM. Noise and Light Pollution in the Hospital: A Call for Action. J Hosp Med. 2017 Oct;12(10):861–862. doi: 10.12788/jhm.2838.
    1. Tang MY, Rhodes S, Powell R, McGowan L, Howarth E, Brown B, Cotterill S. How effective are social norms interventions in changing the clinical behaviours of healthcare workers? A systematic review and meta-analysis. Implement Sci. 2021 Jan 07;16(1):8. doi: 10.1186/s13012-020-01072-1. 10.1186/s13012-020-01072-1
    1. Kiderman A, Ilan U, Gur I, Bdolah-Abram T, Brezis M. Unexplained complaints in primary care: evidence of action bias. J Fam Pract. 2013 Aug;62(8):408–13.jfp_6208g
    1. Haspel RL, Lin Y, Fisher P, Ali A, Parks E, Biomedical Excellence for Safer Transfusion (BEST) Collaborative Development of a validated exam to assess physician transfusion medicine knowledge. Transfusion. 2014 May;54(5):1225–30. doi: 10.1111/trf.12425.
    1. Frank SM, Savage WJ, Rothschild JA, Rivers RJ, Ness PM, Paul SL, Ulatowski JA. Variability in blood and blood component utilization as assessed by an anesthesia information management system. Anesthesiology. 2012 Jul;117(1):99–106. doi: 10.1097/ALN.0b013e318255e550.
    1. Gaglio B, Shoup JA, Glasgow RE. The RE-AIM framework: a systematic review of use over time. Am J Public Health. 2013 Jun;103(6):e38–46. doi: 10.2105/AJPH.2013.301299.
    1. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999 Sep;89(9):1322–7.
    1. Bartels C, Goetz S, Ward E, Carnes M. Internal medicine residents' perceived ability to direct patient care: impact of gender and experience. J Womens Health (Larchmt) 2008 Dec;17(10):1615–21. doi: 10.1089/jwh.2008.0798.
    1. Patel D, Hawkins J, Chehab LZ, Martin-Tuite P, Feler J, Tan A, Alpers BS, Pink S, Wang J, Freise J, Kim P, Peabody C, Bowditch J, Williams ER, Sammann A. Developing Virtual Reality Trauma Training Experiences Using 360-Degree Video: Tutorial. J Med Internet Res. 2020 Dec 16;22(12):e22420. doi: 10.2196/22420. v22i12e22420
    1. Rouleau G, Pelletier J, Côté J, Gagnon M, Martel-Laferrière V, Lévesque R, SimforHealth. Fontaine G. Codeveloping a Virtual Patient Simulation to Foster Nurses' Relational Skills Consistent With Motivational Interviewing: A Situation of Antiretroviral Therapy Nonadherence. J Med Internet Res. 2020 Jul 15;22(7):e18225. doi: 10.2196/18225. v22i7e18225
    1. Padilha JM, Machado PP, Ribeiro A, Ramos J, Costa P. Clinical Virtual Simulation in Nursing Education: Randomized Controlled Trial. J Med Internet Res. 2019 Mar 18;21(3):e11529. doi: 10.2196/11529. doi: 10.2196/11529.v21i3e11529
    1. Eva KW, Norman GR. Heuristics and biases--a biased perspective on clinical reasoning. Med Educ. 2005 Oct;39(9):870–2. doi: 10.1111/j.1365-2929.2005.02258.x.MED2258
    1. Wegwarth O, Gaissmaier W, Gigerenzer G. Smart strategies for doctors and doctors-in-training: heuristics in medicine. Med Educ. 2009 Aug;43(8):721–8. doi: 10.1111/j.1365-2923.2009.03359.x.MED3359
    1. Marewski JN, Gigerenzer G. Heuristic decision making in medicine. Dialogues Clin Neurosci. 2012 Mar;14(1):77–89.

Source: PubMed

3
Subscribe