User Actions within a Clinical Decision Support Alert for the Management of Hypertension in Chronic Kidney Disease

Abstract

Objective

This study aimed to examine user actions within a clinical decision support (CDS) alert addressing hypertension (HTN) in chronic kidney disease (CKD).

Methods

A pragmatic randomized controlled trial of a CDS alert for primary care patients with CKD and uncontrolled blood pressure included prechecked default orders for medication initiation or titration, basic metabolic panel (BMP), and nephrology electronic consult (e-consult). We examined each type of action and calculated percentages of placed and signed orders for subgroups of firings.

Results

There were firings for medication initiation (813) and medication titration (430), and every firing also included orders for nephrology e-consult (1,243) and BMP (1,243). High rates of override (59.6%) and deferral (14.6%) were observed, and CDS-recommended orders were only signed about one-third of the time from within the alert. The percentage of orders that were signed after being placed within the alert was higher for medication initiation than for medication titration (33 vs. 12.0% for angiotensin-converting enzyme inhibitors [ACEi] and 38.8 vs. 14% for angiotensin II receptor blockers [ARBs]). Findings suggest that users are hesitant to commit to immediate action within the alert.

Conclusion

Evaluating user interaction within alerts reveals nuances in physician preferences and workflow that should inform CDS alert design. This study is registered with the Clinicaltrials.gov Trial Registration (identifier: NCT03679247).

Keywords

clinical decision support - alert - override reasons - user actions - feedback

Note

Neither this manuscript, nor the data it contain, has been previously presented at any meetings, or in any other forum.

Authors' Contributions

L.S. had full access to all the data in the study and agrees to take responsibility for the integrity of the data and the accuracy of the data analysis.

Data Sharing Statement

To protect patient privacy and confidentiality, we will not be sharing individual-level de-identified data. Aggregate datasets will be made available upon reasonable request.

Protection of Human and Animal Subjects

This study was approved by the Mass General Brigham Institutional Review Board.

Role of the Funder/Sponsor

The National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

^* These authors should be considered co-first authors.

Publikationsverlauf

Eingereicht: 31. Mai 2024

Angenommen: 19. Dezember 2024

Accepted Manuscript online:
17. März 2025

Artikel online veröffentlicht:
02. Juli 2025

• References

• 1 Sim I, Gorman P, Greenes RA. et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
• 2 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
• 3 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348 (25) 2526-2534
• 4 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
• 5 Wright A, Pang J, Feblowitz JC. et al. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. J Am Med Inform Assoc 2012; 19 (04) 555-561
• 6 Parchman ML, Pugh JA, Romero RL, Bowers KW. Competing demands or clinical inertia: the case of elevated glycosylated hemoglobin. Ann Fam Med 2007; 5 (03) 196-201
• 7 Hicks PC, Westfall JM, Van Vorst RF. et al. Action or inaction? Decision making in patients with diabetes and elevated blood pressure in primary care. Diabetes Care 2006; 29 (12) 2580-2585
• 8 Jaén CR, Stange KC, Nutting PA. Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract 1994; 38 (02) 166-171
• 9 Nutting PA, Baier M, Werner JJ, Cutter G, Conry C, Stewart L. Competing demands in the office visit: what influences mammography recommendations?. J Am Board Fam Pract 2001; 14 (05) 352-361
• 10 Nutting PA, Rost K, Smith J, Werner JJ, Elliot C. Competing demands from physical problems: effect on initiating and completing depression care over 6 months. Arch Fam Med 2000; 9 (10) 1059-1064
• 11 Rost K, Nutting P, Smith J, Coyne JC, Cooper-Patrick L, Rubenstein L. The role of competing demands in the treatment provided primary care patients with major depression. Arch Fam Med 2000; 9 (02) 150-154
• 12 Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med 1998; 338 (21) 1516-1520
• 13 Presseau J, Sniehotta FF, Francis JJ, Campbell NC. Multiple goals and time constraints: perceived impact on physicians' performance of evidence-based behaviours. Implement Sci 2009; 4: 77
• 14 Beasley JW, Wetterneck TB, Temte J. et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med 2011; 24 (06) 745-751
• 15 Zhang J, Patel VL, Johnson TR, Shortliffe EH. A cognitive taxonomy of medical errors. J Biomed Inform 2004; 37 (03) 193-204
• 16 Nijor S, Rallis G, Lad N, Gokcen E. Patient safety issues from information overload in electronic medical records. J Patient Saf 2022; 18 (06) e999-e1003
• 17 Moy AJ, Hobensack M, Marshall K. et al. Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. J Am Med Inform Assoc 2023; 30 (05) 797-808
• 18 Vest BM, York TR, Sand J, Fox CH, Kahn LS. Chronic kidney disease guideline implementation in primary care: A qualitative report from the TRANSLATE CKD Study. J Am Board Fam Med 2015; 28 (05) 624-631
• 19 Allen AS, Forman JP, Orav EJ, Bates DW, Denker BM, Sequist TD. Primary care management of chronic kidney disease. J Gen Intern Med 2011; 26 (04) 386-392
• 20 Vassalotti JA, Boucree SC. Integrating CKD into US primary care: Bridging the knowledge and implementation gaps. Kidney Int Rep 2022; 7 (03) 389-396
• 21 Carroll JK, Pulver G, Dickinson LM. et al. Effect of 2 clinical decision support strategies on chronic kidney disease outcomes in primary care: A cluster randomized trial. JAMA Netw Open 2018; 1 (06) e183377
• 22 Peralta CA, Livaudais-Toman J, Stebbins M. et al. Electronic decision support for management of CKD in primary care: A pragmatic randomized trial. Am J Kidney Dis 2020; 76 (05) 636-644
• 23 Ennis J, Gillen D, Rubenstein A. et al. Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study. BMC Nephrol 2015; 16: 163
• 24 Litvin CB, Hyer JM, Ornstein SM. Use of clinical decision support to improve primary care identification and management of chronic kidney disease (CKD). J Am Board Fam Med 2016; 29 (05) 604-612
• 25 Litvin CB, Nietert PJ, Jenkins RG, Wessell AM, Nemeth LS, Ornstein SM. Translating CKD research into primary care practice: a group-randomized study. J Gen Intern Med 2020; 35 (05) 1435-1443
• 26 Sequist TD, Holliday AM, Orav EJ, Bates DW, Denker BM. Physician and patient tools to improve chronic kidney disease care. Am J Manag Care 2018; 24 (04) e107-e114
• 27 Abdel-Kader K, Fischer GS, Li J, Moore CG, Hess R, Unruh ML. Automated clinical reminders for primary care providers in the care of CKD: a small cluster-randomized controlled trial. Am J Kidney Dis 2011; 58 (06) 894-902
• 28 Drawz PE, Miller RT, Singh S, Watts B, Kern E. Impact of a chronic kidney disease registry and provider education on guideline adherence–a cluster randomized controlled trial. BMC Med Inform Decis Mak 2012; 12: 62
• 29 Tuot DS, Rubinsky AD, Velasquez A. et al. Interventions to improve blood pressure control among socioeconomically disadvantaged patients with CKD: Kidney Awareness Registry and Education Pilot Randomized Controlled Trial. Kidney Med 2019; 1 (05) 242-252
• 30 Samal L, Kilgallon JL, Lipsitz S. et al. Clinical decision support for hypertension management in chronic kidney disease: A randomized clinical trial. JAMA Intern Med 2024; 184 (05) 484-492
• 31 Kilgallon JL, Gannon M, Burns Z. et al. Multicomponent intervention to improve blood pressure management in chronic kidney disease: a protocol for a pragmatic clinical trial. BMJ Open 2021; 11 (12) e054065
• 32 Garabedian PM, Gannon MP, Aaron S, Wu E, Burns Z, Samal L. Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med Inform Decis Mak 2022; 22 (01) 217
• 33 Gannon MP, Wu E, McMahon GM. et al. Uncontrolled blood pressure and treatment of hypertension in older chronic kidney disease patients. J Am Geriatr Soc 2021; 69 (10) 2985-2987
• 34 Samal L, Wu E, Aaron S. et al. Refining clinical phenotypes to improve clinical decision support and reduce alert fatigue: A feasibility study. Appl Clin Inform 2023; 14 (03) 528-537
• 35 Dorr DA, D'Autremont C, Pizzimenti C. et al. Assessing data adequacy for high blood pressure clinical decision support: A quantitative analysis. Appl Clin Inform 2021; 12 (04) 710-720
• 36 Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 2009; 23 (04) 38-45
• 37 Nevin C, Shawwa K, Pincavitch J. et al. Acceptance of acute kidney injury alert by providers in cardiac surgery intensive care unit. Appl Clin Inform 2023; 14 (01) 119-127
• 38 Sittig DF, Wright A, Osheroff JA. et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392
• 39 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
• 40 Meunier PY, Raynaud C, Guimaraes E, Gueyffier F, Letrilliart L. Barriers and facilitators to the use of clinical decision support systems in primary care: A mixed-methods systematic review. Ann Fam Med 2023; 21 (01) 57-69
• 41 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. with the HITEC Investigators. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak 2017; 17 (01) 36
• 42 Blecker S, Pandya R, Stork S. et al. Interruptive versus noninterruptive clinical decision support: Usability study. JMIR Hum Factors 2019; 6 (02) e12469
• 43 Jones BE, Collingridge DS, Vines CG. et al. CDS in a Learning Health Care System: Identifying physicians' reasons for rejection of best-practice recommendations in pneumonia through computerized clinical decision support. Appl Clin Inform 2019; 10 (01) 1-9
• 44 Aaron S, McEvoy DS, Ray S, Hickman TT, Wright A. Cranky comments: detecting clinical decision support malfunctions through free-text override reasons. J Am Med Inform Assoc 2019; 26 (01) 37-43
• 45 Wright A, McEvoy DS, Aaron S. et al. Structured override reasons for drug-drug interaction alerts in electronic health records. J Am Med Inform Assoc 2019; 26 (10) 934-942
• 46 Rubins D, McCoy AB, Dutta S. et al. Real-time user feedback to support clinical decision support system improvement. Appl Clin Inform 2022; 13 (05) 1024-1032
• 47 Sheth HS, Grimes VD, Rudge D. et al. Improving pneumococcal vaccination rates in rheumatology patients by using best practice alerts in the electronic health records. J Rheumatol 2021; 48 (09) 1472-1479
• 48 Goldstein JE, Guo X, Swenor BK, Boland MV, Smith K. Using electronic clinical decision support to examine vision rehabilitation referrals and practice guidelines in ophthalmology. Transl Vis Sci Technol 2022; 11 (10) 8
• 49 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
• 50 Powers EM, Shiffman RN, Melnick ER, Hickner A, Sharifi M. Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review. J Am Med Inform Assoc 2018; 25 (11) 1556-1566