Clinical Decision Support for Fall Prevention: Defining End-User Needs

Abstract

Background and Significance Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations.

Objectives The goal of this research is to identify end-user (primary care staff and patients) needs through a human-centered design process for a tool that will generate CDS to protect older adults from falls and injuries.

Methods Primary care staff (primary care providers, care coordinator nurses, licensed practical nurses, and medical assistants) and community-dwelling patients aged 60 years or older associated with Brigham & Women's Hospital-affiliated primary care clinics and the University of Florida Health Archer Family Health Care primary care clinic were eligible to participate in this study. Through semi-structured and exploratory interviews with participants, our team identified end-user needs through content analysis.

Results User needs for primary care staff (n = 24) and patients (n = 18) were categorized under the following themes: workload burden; systematic communication; in-person assessment of patient condition; personal support networks; motivational tools; patient understanding of fall risk; individualized resources; and evidence-based safe exercises and expert guidance. While some of these themes are specific to either primary care staff or patients, several address needs expressed by both groups of end-users.

Conclusion Our findings suggest that there are many care gaps in fall prevention management in primary care and that personalized, actionable, and evidence-based CDS has the potential to address some of these gaps.

Keywords

clinical decision support - primary care - human-centered design - user needs - falls

Protection of Human and Animal Subjects

This study was approved by Partners HealthCare Humans Research Committee under protocol number 2020P002075. The committee granted implied consent by voluntary participation in the study.

Author Contributions

P.M.G., R.I.B., N.K.L., R.J.L., D.S., and P.C.D. were involved in the design of the study. H.R., P.M.G., and K.S. were involved in the coding of the data. All authors were involved in the analysis of the data. H.R. and P.M.G wrote the manuscript. All authors read and approved the final manuscript.

Publikationsverlauf

Eingereicht: 31. Januar 2022

Angenommen: 04. Mai 2022

Artikel online veröffentlicht:
29. Juni 2022

• References

• 1 Kenny RAM, Rubenstein LZ, Tinetti ME. et al; Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc 2011; 59 (01) 148-157
• 2 Katsulis Z, Ergai A, Leung WY. et al. Iterative user centered design for development of a patient-centered fall prevention toolkit. Appl Ergon 2016; 56: 117-126
• 3 Dykes PC, Carroll DL, Hurley A. et al. Fall TIPS: strategies to promote adoption and use of a fall prevention toolkit. AMIA Annu Symp Proc 2009; 2009: 153-157
• 4 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 (01) 17
• 5 Kawamoto K, McDonald CJ. Designing, conducting, and reporting clinical decision support studies: recommendations and call to action. Ann Intern Med 2020; 172 (11, Suppl): S101-S109
• 6 Nanji KC, Seger DL, Slight SP. et al. Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc 2018; 25 (05) 476-481
• 7 Yoo J, Lee J, Rhee PL. et al. Alert override patterns with a medication clinical decision support system in an academic emergency department: retrospective descriptive study. JMIR Med Inform 2020; 8 (11) e23351
• 8 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
• 9 Marcial LH, Blumenfeld B, Harle C. et al. Barriers, facilitators, and potential solutions to advancing interoperable clinical decision support: multi-stakeholder consensus recommendations for the opioid use case. AMIA Annu Symp Proc 2020; 2019: 637-646
• 10 Tricco AC, Thomas SM, Veroniki AA. et al. Quality improvement strategies to prevent falls in older adults: a systematic review and network meta-analysis. Age Ageing 2019; 48 (03) 337-346
• 11 Lytle KS, Short NM, Richesson RL, Horvath MM. Clinical decision support for nurses: a fall risk and prevention example. Comput Inform Nurs 2015; 33 (12) 530-537 , quiz E1
• 12 Brunner J, Chuang E, Goldzweig C, Cain CL, Sugar C, Yano EM. User-centered design to improve clinical decision support in primary care. Int J Med Inform 2017; 104: 56-64
• 13 ISO. 14:00–17:00. ISO 9241–210:2019. Accessed August 18, 2021 at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/07/75/77520.html
• 14 Luna DR, Rizzato Lede DA, Rubin L. et al. User-centered design improves the usability of drug-drug interaction alerts: a validation study in the real scenario. Stud Health Technol Inform 2017; 245: 1085-1089
• 15 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
• 16 Yu CH, Stacey D, Sale J. et al. Designing and evaluating an interprofessional shared decision-making and goal-setting decision aid for patients with diabetes in clinical care–systematic decision aid development and study protocol. Implement Sci 2014; 9: 16
• 17 NIST. [e-mail]. Human Centered Design (HCD). Published April 12, 2021. Accessed March 14, 2022 at: https://www.nist.gov/itl/iad/visualization-and-usability-group/human-factors-human-centered-design
• 18 ISO/IEC 25066:2016(en), Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Common Industry Format (CIF) for Usability — Evaluation Report. Accessed July 26, 2021 at: https://www.iso.org/obp/ui/#iso:std:iso-iec:25066:ed-1:v1:en
• 19 Lucero RJ, Yoon S, Suero-Tejeda N. et al. Application of persuasive systems design principles to design a self-management application user interface for Hispanic informal dementia caregivers: user preferences and perceptions. JAMIA Open 2022; 5 (01) b114
• 20 Nanji KC, Garabedian PM, Shaikh SD. et al. Development of a perioperative medication-related clinical decision support tool to prevent medication errors: an analysis of user feedback. Appl Clin Inform 2021; 12 (05) 984-995
• 21 Bhasin S, Gill TM, Reuben DB. et al; STRIDE Trial Investigators. A randomized trial of a multifactorial strategy to prevent serious fall injuries. N Engl J Med 2020; 383 (02) 129-140
• 22 Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res 2005; 15 (09) 1277-1288
• 23 Hruschka DJ, Schwartz D, St John DC, Picone-Decaro E, Jenkins RA, Carey JW. Reliability in coding open-ended data: lessons learned from HIV behavioral research. Field Methods 2004; 16 (03) 307-331
• 24 Gulla J, Neri PM, Bates DW, Samal L. User requirements for a chronic kidney disease clinical decision support tool to promote timely referral. Int J Med Inform 2017; 101: 50-57
• 25 Yarnall KSH, Pollak KI, Østbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention?. Am J Public Health 2003; 93 (04) 635-641
• 26 Abbo ED, Zhang Q, Zelder M, Huang ES. The increasing number of clinical items addressed during the time of adult primary care visits. J Gen Intern Med 2008; 23 (12) 2058-2065
• 27 Privett N, Guerrier S. Estimation of the time needed to deliver the 2020 USPSTF preventive care recommendations in primary care. Am J Public Health 2021; 111 (01) 145-149
• 28 Otago Exercise Programme to Prevent Falls in Older Adults Wellington, N.Z.. ACC; 2003
• 29 Fortinsky RH, Iannuzzi-Sucich M, Baker DI. et al. Fall-risk assessment and management in clinical practice: views from healthcare providers. J Am Geriatr Soc 2004; 52 (09) 1522-1526
• 30 Jones TS, Ghosh TS, Horn K, Smith J, Vogt RL. Primary care physicians perceptions and practices regarding fall prevention in adult's 65 years and over. Accid Anal Prev 2011; 43 (05) 1605-1609
• 31 McConville A, Hooven K. Factors influencing the implementation of falls prevention practice in primary care. J Am Assoc Nurse Pract 2020; 33 (02) 108-116
• 32 Casey CM, Parker EM, Winkler G, Liu X, Lambert GH, Eckstrom E. Lessons learned from implementing CDC's STEADI falls prevention algorithm in primary care. Gerontologist 2017; 57 (04) 787-796
• 33 Kruschke C, Butcher HK. Evidence-based practice guideline: fall prevention for older adults. J Gerontol Nurs 2017; 43 (11) 15-21
• 34 Lach HW, Noimontree W. Fall prevention among community-dwelling older adults: current guidelines and older adult responses. J Gerontol Nurs 2018; 44 (09) 21-29
• 35 Moreland B, Kakara R, Henry A. Trends in nonfatal falls and fall-related injuries among adults aged ≥65 years - United States, 2012-2018. Morb Mortal Wkly Rep 2020; 69 (27) 875-881