Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative AnalysisFunding This work was supported by AHRQ grant U18 HS26849–01.
Objective This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools.
Methods We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS.
Results We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9–11.2%), low exclusion (0–0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting.
Discussion Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases.
Conclusion Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.
Protection of Human and Animal Subjects
Human and animal subjects were not included in this project. This work was approved by the Oregon Health and Science University Institutional Review Board.
Eingereicht: 15. Januar 2021
Angenommen: 04. Juni 2021
04. August 2021 (online)
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 Fryar CD, Ostchega Y, Hales CM, Zhang G, Kruszon-Moran D. Hypertension prevalence and control among adults: United States, 2015-2016. NCHS Data Brief 2017; ; Oct (289) 1-8
- 2 Zonneveld TP, Richard E, Vergouwen MD. et al. Blood pressure-lowering treatment for preventing recurrent stroke, major vascular events, and dementia in patients with a history of stroke or transient ischaemic attack. Cochrane Database Syst Rev 2018; 7: CD007858
- 3 Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360 (9349): 1903-1913
- 4 Hogan WR, Wagner MM. Accuracy of data in computer-based patient records. J Am Med Inform Assoc 1997; 4 (05) 342-355
- 5 Thiru K, Hassey A, Sullivan F. Systematic review of scope and quality of electronic patient record data in primary care. BMJ 2003; 326 (7398): 1070
- 6 Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev 2010; 67 (05) 503-527
- 7 Kahn MG, Callahan TJ, Barnard J. et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS (Wash DC) 2016; 4 (01) 1244
- 8 Berner ES, Kasiraman RK, Yu F, Ray MN, Houston TK. Data quality in the outpatient setting: impact on clinical decision support systems. AMIA Annu Symp Proc 2005; 2005: 41-45
- 9 Glynn L, Casey M, Walsh J, Hayes PS, Harte RP, Heaney D. Patients' views and experiences of technology based self-management tools for the treatment of hypertension in the community: a qualitative study. BMC Fam Pract 2015; 16: 119
- 10 United States Preventive Services Task Force. Final recommendation statement: hypertension in adults: screening. April 27, 2021. Available at: https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/hypertension-in-adults-screening
- 11 Whelton PK, Einhorn PT, Muntner P. et al; National Heart, Lung, and Blood Institute Working Group on Research Needs to Improve Hypertension Treatment and Control in African Americans. Research needs to improve hypertension treatment and control in African Americans. Hypertension 2016; 68 (05) 1066-1072
- 12 Nguyen-Huynh MN, Hills NK, Sidney S, Klingman JG, Johnston SC. Race-ethnicity on blood pressure control after ischemic stroke: a prospective cohort study. J Am Soc Hypertens 2017; 11 (01) 38-44
- 13 Carey RM, Calhoun DA, Bakris GL. et al; American Heart Association Professional/Public Education and Publications Committee of the Council on Hypertension; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Genomic and Precision Medicine; Council on Peripheral Vascular Disease; Council on Quality of Care and Outcomes Research; and Stroke Council. Resistant hypertension: detection, evaluation, and management: a scientific statement from the American Heart Association. Hypertension 2018; 72 (05) e53-e90
- 14 Qaseem A, Wilt TJ, Rich R, Humphrey LL, Frost J, Forciea MA. Clinical Guidelines Committee of the American College of Physicians and the Commission on Health of the Public and Science of the American Academy of Family Physicians. Pharmacologic treatment of hypertension in adults aged 60 years or older to higher versus lower blood pressure targets: a clinical practice guideline from the American College of Physicians and the American Academy of Family Physicians. Ann Intern Med 2017; 166 (06) 430-437
- 15 Wright Jr JT, Williamson JD, Whelton PK. et al; SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med 2015; 373 (22) 2103-2116
- 16 Prevention CfDCa. Interactive protocol for controlling hypertension. . Accessed July 5, 2021 at: https://nccd.cdc.gov/MillionHearts/Protocol/
- 17 Persson M, Mjörndal T, Carlberg B, Bohlin J, Lindholm LH. Evaluation of a computer-based decision support system for treatment of hypertension with drugs: retrospective, nonintervention testing of cost and guideline adherence. J Intern Med 2000; 247 (01) 87-93
- 18 Roumie CL, Elasy TA, Greevy R. et al. Improving blood pressure control through provider education, provider alerts, and patient education: a cluster randomized trial. Ann Intern Med 2006; 145 (03) 165-175
- 19 Bosworth HB, Olsen MK, McCant F. et al. Hypertension Intervention Nurse Telemedicine Study (HINTS): testing a multifactorial tailored behavioral/educational and a medication management intervention for blood pressure control. Am Heart J 2007; 153 (06) 918-924
- 20 Hicks LS, Sequist TD, Ayanian JZ. et al. Impact of computerized decision support on blood pressure management and control: a randomized controlled trial. J Gen Intern Med 2008; 23 (04) 429-441
- 21 Anchala R, Kaptoge S, Pant H, Di Angelantonio E, Franco OH, Prabhakaran D. Evaluation of effectiveness and cost-effectiveness of a clinical decision support system in managing hypertension in resource constrained primary health care settings: results from a cluster randomized trial. J Am Heart Assoc 2015; 4 (01) e001213
- 22 Rinfret S, Lussier MT, Peirce A. et al; LOYAL Study Investigators. The impact of a multidisciplinary information technology-supported program on blood pressure control in primary care. Circ Cardiovasc Qual Outcomes 2009; 2 (03) 170-177
- 23 Nair M, Ali MK, Ajay VS. et al. CARRS Surveillance study: design and methods to assess burdens from multiple perspectives. BMC Public Health 2012; 12: 701
- 24 Fix GM, Cohn ES, Solomon JL. et al. The role of comorbidities in patients' hypertension self-management. Chronic Illn 2014; 10 (02) 81-92
- 25 Fletcher BR, Hinton L, Hartmann-Boyce J, Roberts NW, Bobrovitz N, McManus RJ. Self-monitoring blood pressure in hypertension, patient and provider perspectives: a systematic review and thematic synthesis. Patient Educ Couns 2016; 99 (02) 210-219
- 26 Herrera PA, Moncada L, Defey D. Understanding non-adherence from the inside: hypertensive patients' motivations for adhering and not adhering. Qual Health Res 2017; 27 (07) 1023-1034
- 27 Ma C. An investigation of factors influencing self-care behaviors in young and middle-aged adults with hypertension based on a health belief model. Heart Lung 2018; 47 (02) 136-141
- 28 Omboni S, Ferrari R. The role of telemedicine in hypertension management: focus on blood pressure telemonitoring. Curr Hypertens Rep 2015; 17 (04) 535
- 29 Gibson B, Butler J, Schnock K, Bates D, Classen D. Design of a safety dashboard for patients. Patient Educ Couns 2020; 103 (04) 741-747
- 30 Goehringer JM, Bonhag MA, Jones LK. et al. Generation and implementation of a patient-centered and patient-facing genomic test report in the EHR. EGEMS (Wash DC) 2018; 6 (01) 14
- 31 Weissman GE, Yadav KN, Madden V. et al. Numeracy and understanding of quantitative aspects of predictive models: a pilot study. Appl Clin Inform 2018; 9 (03) 683-692
- 32 Ghimire S, Shrestha N, Callahan K. Barriers to dietary salt reduction among hypertensive patients. J Nepal Health Res Counc 2018; 16 (02) 124-130
- 33 Johnson RA, Huntley A, Hughes RA. et al. Interventions to support shared decision making for hypertension: a systematic review of controlled studies. Health Expect 2018; 21 (06) 1191-1207
- 34 International HLS. FHIR® – fast healthcare interoperability resources 2019,. November 1. Accessed July 5, 2021 at: http://hl7.org/fhir/
- 35 21st Century Cures Act, H.R. 34. 2016.
- 36 Services UDoHH. Pharmacist eCare Plan: HealthIT.gov. Accessed July 5, 2021 at: https://www.healthit.gov/techlab/ipg/node/4/submission/1376
- 37 Cinical Guidelines FHIR. (v0.1.0) (STU1 Ballot): HL7; 2019. Accessed July 5, 2021 at: http://hl7.org/fhir/uv/cpg/2019Sep/index.html#home
- 38 McClure RC, Macumber CL, Skapik JL, Smith AM. Igniting harmonized digital clinical quality measurement through terminology, CQL, and FHIR. Appl Clin Inform 2020; 11 (01) 23-33
- 39 Alper BS, Price A, van Zuuren EJ. et al. Consistency of recommendations for evaluation and management of hypertension. JAMA Netw Open 2019; 2 (11) e1915975
- 40 Suchard MA, Schuemie MJ, Krumholz HM. et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet 2019; 394 (10211): 1816-1826
- 41 Martijn Schuemie MS. OHDSI Large-Scale Evidence Generation and Evaluation in a Network of Databases (LEGEND) 2018. Accessed July 5, 2021 at: https://github.com/OHDSI/Legend
- 42 Aveyard P, Begh R, Parsons A, West R. Brief opportunistic smoking cessation interventions: a systematic review and meta-analysis to compare advice to quit and offer of assistance. Addiction 2012; 107 (06) 1066-1073
- 43 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007; 2007: 26-30
- 44 Gadhiya K, Zamora E, Saiyed SM, Friedlander D, Kaelber DC. Drug alert experience and salience during medical residency at two healthcare institutions. Appl Clin Inform 2021; 12 (02) 355-361
- 45 Olakotan OO, Mohd Yusof M. The appropriateness of clinical decision support systems alerts in supporting clinical workflows: a systematic review. Health Informatics J 2021;27(02):14604582211007536
- 46 Perna G. Trend: patient-generated health data. Transforming data into decision support. The advance of patient-facing connected technologies will allow providers to track patients on a daily basis. Healthc Inform 2014; 31 (02) 26-27
- 47 Ralston JD, Cook AJ, Anderson ML. et al. Home blood pressure monitoring, secure electronic messaging and medication intensification for improving hypertension control: a mediation analysis. Appl Clin Inform 2014; 5 (01) 232-248
- 48 Benkert R, Dennehy P, White J, Hamilton A, Tanner C, Pohl JM. Diabetes and hypertension quality measurement in four safety-net sites: lessons learned after implementation of the same commercial electronic health record. Appl Clin Inform 2014; 5 (03) 757-772
- 49 Carrera A, Pifarré M, Vilaplana J. et al. BPcontrol. A mobile app to monitor hypertensive patients. Appl Clin Inform 2016; 7 (04) 1120-1134
- 50 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
- 51 Sieja A, Markley K, Pell J. et al. Optimization sprints: improving clinician satisfaction and teamwork by rapidly reducing electronic health record burden. Mayo Clin Proc 2019; 94 (05) 793-802