Clinicians' Values and Preferences for Medication Adherence and Cost Clinical Decision Support in Primary Care: A Qualitative Study

Abstract

Background Medication nonadherence and unaffordability are prevalent, burdensome issues in primary care. In response, technology companies are capitalizing on clinical decision support (CDS) to deliver patient-specific information regarding medication adherence and costs to clinicians using electronic health records (EHRs). To maximize adoption and usability, these CDS tools should be designed with consideration of end users' values and preferences.

Objective This article evaluates primary care clinicians' values and preferences for a medication adherence and cost CDS.

Methods We conducted semistructured interviews with primary care clinicians with prescribing privileges and EHR access to identify clinicians' perceptions of and approaches to assessing medication adherence and costs, and to determine perceived values and preferences for medication adherence and cost CDS. Interviews were conducted until saturation of responses was reached. ATLAS.ti was used for thematic analysis.

Results Among 26 clinicians interviewed, themes identified included a high value, but moderate need for a medication adherence CDS and high value and need for cost CDS. Clinicians expressed the cost CDS would provide actionable solutions and greatly impact patient care. Another theme identified was a desire for medication adherence and cost CDS to be separate tools yet integrated into workflow. The majority of clinicians preferred a medication adherence CDS that integrated claims data and actively displayed data using color-coded adherence categories within patients' medication lists in the EHR. For the cost CDS, clinicians preferred medication out-of-pocket costs and a list of cheaper or payor-preferred alternatives to display within the order queue of the EHR.

Conclusion We identified valuable insights regarding clinician values and preferences for medication adherence and cost CDS. Overall, primary care clinicians feel CDS for medication adherence and cost are valuable and prefer them to be separate. These insights should be used to inform the design, implementation, and EHR integration of future medication and cost CDS tools.

Keywords

clinical decision support - medication adherence - medication costs - perceptions - primary care - medication - informatics

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.

• References

• 1 Chronic disease overview. Centers for Disease Control and Prevention Web site. Published June 28, 2017. Updated June 28, 2017. Available at: https://www.cdc.gov/chronicdisease/overview/index.htm . Accessed January 15, 2018
• 2 Multiple chronic conditions. Centers for Disease Control and Prevention Web site. Published February 12, 2015. Updated January 20, 2016. Available at: https://www.cdc.gov/chronicdisease/about/multiple-chronic.htm . Accessed January 15, 2018
• 3 Ruzicka M, Hiremath S. Can drugs work in patients who do not take them? The problem of non-adherence in resistant hypertension. Curr Hypertens Rep 2015; 17 (09) 579
• 4 Wagner TH, Heisler M, Piette JD. Prescription drug co-payments and cost-related medication underuse. Health Econ Policy Law 2008; 3 (Pt 1): 51-67
• 5 Fischer MA, Stedman MR, Lii J. , et al. Primary medication non-adherence: analysis of 195,930 electronic prescriptions. J Gen Intern Med 2010; 25 (04) 284-290
• 6 McHorney CA, Victor Spain C, Alexander CM, Simmons J. Validity of the adherence estimator in the prediction of 9-month persistence with medications prescribed for chronic diseases: a prospective analysis of data from pharmacy claims. Clin Ther 2009; 31 (11) 2584-2607
• 7 Hayakawa M, Uchimura Y, Omae K, Waki K, Fujita H, Ohe K. A smartphone-based medication self-management system with realtime medication monitoring. Appl Clin Inform 2013; 4 (01) 37-52
• 8 Quintana Y, Gonzalez Martorell EA, Fahy D, Safran C. A systematic review on promoting adherence to antiretroviral therapy in HIV-infected patients using mobile phone technology. Appl Clin Inform 2018; 9 (02) 450-466
• 9 Farzandipour M, Nabovati E, Sharif R, Arani MH, Anvari S. Patient self-management of asthma using mobile health applications: a systematic review of the functionalities and effects. Appl Clin Inform 2017; 8 (04) 1068-1081
• 10 Pernell BM, DeBaun MR, Becker K, Rodeghier M, Bryant V, Cronin RM. Improving medication adherence with two-way short message service reminders in sickle cell disease and asthma. A feasibility randomized controlled trial. Appl Clin Inform 2017; 8 (02) 541-559
• 11 Why our drugs cost so much. American Association of Retired Persons (AARP) Web site. Published May 2017. Available at: https://www.aarp.org/health/drugs-supplements/info-2017/rx-prescription-drug-pricing.html . Accessed January 15, 2018
• 12 Copher R, Buzinec P, Zarotsky V, Kazis L, Iqbal SU, Macarios D. Physician perception of patient adherence compared to patient adherence of osteoporosis medications from pharmacy claims. Curr Med Res Opin 2010; 26 (04) 777-785
• 13 Meddings J, Kerr EA, Heisler M, Hofer TP. Physician assessments of medication adherence and decisions to intensify medications for patients with uncontrolled blood pressure: still no better than a coin toss. BMC Health Serv Res 2012; 12: 270
• 14 Mushlin AI, Appel FA. Diagnosing potential noncompliance. Physicians' ability in a behavioral dimension of medical care. Arch Intern Med 1977; 137 (03) 318-321
• 15 Burkholder R, Linn E. So simple, so hard: taking medication as directed. Res Social Adm Pharm 2017; 13 (01) 233-249
• 16 Alexander GC, Casalino LP, Meltzer DO. Physician strategies to reduce patients' out-of-pocket prescription costs. Arch Intern Med 2005; 165 (06) 633-636
• 17 Alexander GC, Casalino LP, Tseng CW, McFadden D, Meltzer DO. Barriers to patient-physician communication about out-of-pocket costs. J Gen Intern Med 2004; 19 (08) 856-860
• 18 Alexander GC, Casalino LP, Meltzer DO. Patient-physician communication about out-of-pocket costs. JAMA 2003; 290 (07) 953-958
• 19 Tarn DM, Paterniti DA, Heritage J, Hays RD, Kravitz RL, Wenger NS. Physician communication about the cost and acquisition of newly prescribed medications. Am J Manag Care 2006; 12 (11) 657-664
• 20 Piette JD, Heisler M, Wagner TH. Cost-related medication underuse: do patients with chronic illnesses tell their doctors?. Arch Intern Med 2004; 164 (16) 1749-1755
• 21 Surescripts transforms prescription decision process between physicians and patients. SureScripts Web site. Published November 7, 2017. Available at: http://surescripts.com/news-center/press-releases/!content/surescripts-transforms-prescription-decision-process-between-physicians-and-patients . Accessed January 15, 2018
• 22 Monsen CB, Liao JM, Gaster B, Flynn KJ, Payne TH. The effect of medication cost transparency alerts on prescriber behavior. J Am Med Inform Assoc 2019; 26 (10) 920-927
• 23 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
• 24 Bright TJ, Wong A, Dhurjati R. , et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (01) 29-43
• 25 Bell EJ, Takhar SS, Beloff JR, Schuur JD, Landman AB. Information technology improves Emergency Department patient discharge instructions completeness and performance on a national quality measure: a quasi-experimental study. Appl Clin Inform 2013; 4 (04) 499-514
• 26 Murphy DR, Meyer AN, Russo E, Sittig DF, Wei L, Singh H. The burden of inbox notifications in commercial electronic health records. JAMA Intern Med 2016; 176 (04) 559-560
• 27 Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19 (03) 413-422
• 28 Kashfi H. Applying a user centered design methodology in a clinical context. Stud Health Technol Inform 2010; 160 (Pt 2): 927-931
• 29 McCullagh LJ, Sofianou A, Kannry J, Mann DM, McGinn TG. User centered clinical decision support tools: adoption across clinician training level. Appl Clin Inform 2014; 5 (04) 1015-1025
• 30 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
• 31 Noy C. Sampling knowledge: the hermeneutics of snowball sampling in qualitative research. Int J Soc Res Methodol 2008; 11: 327-344
• 32 Robinson OC. Sampling in interview-based qualitative research: a theoretical and practical guide. Qual Res Psychol 2013; 11: 25-41
• 33 Champion VL, Skinner CS. The health belief model. In: Glanz K, Rimer B, Viswanath K. , eds. Health Behavior and Health Education: Theory, Research, and Practice. San Francisco, CA: Wiley; 2008
• 34 Montaño DE, Kasprzyk D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz K, Rimer BK, Viswanath K. , eds. Health Behavior and Health Education: Theory, Research, and Practice. 4th ed. San Francisco, CA: Wiley; 2008
• 35 Creswell JW. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 2nd ed. Thousand Oaks, CA: Sage; 2003
• 36 Cho JY, Lee EH. Reducing confusion about grounded theory and qualitative content analysis: similarities and differences. Qual Rep 2014; 19: 1
• 37 Glaser B, Strauss A. The Discovery of Grounded Theory: Strategies for Qualitative Research. Chicago, IL: Aldine; 1967
• 38 Fereday J, Muri-Cochrane E. Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. Int J Qual Methods 2006; 5: 1-11
• 39 Ericsson KA, Simon HA. Protocol Analysis – Rev'd Edition: Verbal Reports as Data. Cambridge, MA: Bradford; 1993
• 40 Daly J, Kellehear A, Gliksman M. The Public Health Researcher: A Methodological Guide. Melbourne, Australia: Oxford University Press; 1998
• 41 Crabtree B, Miller W. A template approach to text analysis: developing and using codebooks. In: Crabtree B, Miller W. , ed. Doing Qualitative Research. Newbury Park, CA: Sage; 1999
• 42 Boyatzis R. Transforming Qualitative Information: Thematic Analysis and Code Development. Thousand Oaks, CA: Sage; 1998
• 43 O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med 2014; 89 (09) 1245-1251
• 44 Cypress BS. Rigor or reliability and validity in qualitative research: perspectives, strategies, reconceptualization, and recommendations. Dimens Crit Care Nurs 2017; 36 (04) 253-263
• 45 Tseng CW, Lin GA, Davis J. , et al. Giving formulary and drug cost information to providers and impact on medication cost and use: a longitudinal non-randomized study. BMC Health Serv Res 2016; 16 (01) 499
• 46 Korn LM, Reichert S, Simon T, Halm EA. Improving physicians' knowledge of the costs of common medications and willingness to consider costs when prescribing. J Gen Intern Med 2003; 18 (01) 31-37
• 47 Khan S, Sylvester R, Scott D, Pitts B. Physicians' opinions about responsibility for patient out-of-pocket costs and formulary prescribing in two Midwestern states. J Manag Care Pharm 2008; 14 (08) 780-789
• 48 Fischer MA, Avorn J. Economic implications of evidence-based prescribing for hypertension: can better care cost less?. JAMA 2004; 291 (15) 1850-1856
• 49 Natural Academy of Medicine. Optimizing strategies for clinical decision support: summary of a meeting series. Available at: https://www.healthit.gov/sites/default/files/page/2018-04/Optimizing_Strategies_508.pdf . Accessed April 28, 2020
• 50 Kawamanto K, Flynn MC, Kukhareva P. , et al. A pragmatic guide to establishing clinical decisions support governance and addressing decision support fatigue: a case study. AMIA Annu Symp Proc 2018; 2018: 624-633
• 51 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
• 52 Osheroff J, Teich J, Levick D, Saldana L, Velasco F, Sittig D. Improving Outcomes with Clinical Decision Support: An Implementers' Guide. 2nd ed. Chicago, IL: Healthcare Information Management Systems Society (HIMSS); 2012
• 53 Petterson S, McNellis R, Klink K, Meyers D, Bazemore A. The State of Primary Care in the United States: A Chartbook of Facts and Statistics. January 2018. Available at: https://www.graham-center.org/content/dam/rgc/documents/publications-reports/reports/PrimaryCareChartbook.pdf . Accessed March 3, 2020
• 54 Rathish D, Hemachandra R, Premadasa T. , et al. Comparison of medication adherence between type 2 diabetes mellitus patients who pay for their medications and those who receive it free: a rural Asian experience. J Health Popul Nutr 2019; 38 (01) 4
• 55 Mongkhon P, Ashcroft DM, Scholfield CN, Kongkaew C. Hospital admissions associated with medication non-adherence: a systematic review of prospective observational studies. BMJ Qual Saf 2018; 27 (11) 902-914
• 56 Semahegn A, Torpey K, Manu A, Assefa N, Tesfaye G, Ankomah A. Psychotropic medication non-adherence and its associated factors among patients with major psychiatric disorders: a systematic review and meta-analysis. Syst Rev 2020; 9 (01) 17
• 57 Guerrazzi C. An international perspective on health information exchange: adoption in OECD countries with different health care system configurations. Med Care Res Rev 2019; 1077558719858245
• 58 Hernandez I, Good CB, Cutler DM, Gellad WF, Parekh N, Shrank WH. The contribution of new product entry versus existing product inflation in the rising costs of drugs. Health Aff (Millwood) 2019; 38 (01) 76-83