Appl Clin Inform 2021; 12(03): 621-628
DOI: 10.1055/s-0041-1731341
Research Article

Optimizing Clinical Monitoring Tools to Enhance Patient Review by Pharmacists

Diana J. Schreier
1   Department of Pharmacy, Mayo Clinic, Rochester, Minnesota, United States
Jenna K. Lovely
1   Department of Pharmacy, Mayo Clinic, Rochester, Minnesota, United States
› Author Affiliations


Background The Clinical Monitoring List (CML) is a real-time scoring system and intervention tool used by Mayo Clinic pharmacists caring for hospitalized patients.

Objective The study aimed to describe the iterative development and implementation of pharmacist clinical monitoring tools within the electronic health record at a multicampus health system enterprise.

Methods Between October 2018 and January 2019, pharmacists across the enterprise were surveyed to determine opportunities and gaps in CML functionality. Responses were received from 39% (n = 162) of actively staffing inpatient pharmacists. Survey responses identified three main gaps in CML functionality: (1) the desire for automated checklists of tasks, (2) additional rule logic closely aligning with clinical practice guidelines, and (3) the ability to dismiss and defer rules. The failure mode and effect analysis were used to assess risk areas within the CML. To address identified gaps, two A/B testing pilots were undertaken. The first pilot analyzed the effect of updated CML rule logic on pharmacist satisfaction in the domains of automated checklists and guideline alignment. The second pilot assessed the utility of a Clinical Monitoring Navigator (CMN) functioning in conjunction with the CML to display rules with selections to dismiss or defer rules until a user-specified date. The CMN is a workspace to guide clinical end user workflows; permitting the review and actions to be completed within one screen using EHR functionality.

Results A total of 27 pharmacists across a broad range of practice specialties were selected for two separate two-week pilot tests. Upon pilot completion, participants were surveyed to assess the effect of updates on performance gaps.

Conclusion Findings from the enterprise-wide survey and A/B pilot tests were used to inform final build decisions and planned enterprise-wide updated CML and CMN launch. This project serves as an example of the utility of end-user feedback and pilot testing to inform project decisions, optimize usability, and streamline build activities.

Protection of Human and Animal Subjects

No human interventions were performed as the study iterations were based on the updates of the workflow and tools, rather than the direct patient care being provided.

Publication History

Received: 15 February 2021

Accepted: 17 May 2021

Article published online:
23 June 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

  • References

  • 1 Hauck K, Zhao X. How dangerous is a day in hospital? A model of adverse events and length of stay for medical inpatients. Med Care 2011; 49 (12) 1068-1075
  • 2 Robertson J, Walkom E, Pearson SA, Hains I, Williamsone M, Newby D. The impact of pharmacy computerised clinical decision support on prescribing, clinical and patient outcomes: a systematic review of the literature. Int J Pharm Pract 2010; 18 (02) 69-87
  • 3 Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163 (12) 1409-1416
  • 4 Garg AX, Adhikari NKJ, McDonald H. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238
  • 5 Yan L, Reese T, Nelson SD. A narrative review of clinical decision support for inpatient clinical pharmacists. Appl Clin Inform 2021; 12 (02) 199-20
  • 6 Wilson JW, Oyen LJ, Ou NN. et al. Hospital rules-based system: the next generation of medical informatics for patient safety. Am J Health Syst Pharm 2005; 62 (05) 499-505
  • 7 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; 42 (02) 377-381
  • 8 Shirley D. Project Management for Healthcare. 2nd ed.. Boca Raton, FL: CRC Press; 2020
  • 9 Ellsworth MA, Dziadzko M, O'Horo JC, Farrell AM, Zhang J, Herasevich V. An appraisal of published usability evaluations of electronic health records via systematic review. J Am Med Inform Assoc 2017; 24 (01) 218-226
  • 10 Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19 (03) 413-422
  • 11 Golob Jr JF, Fadlalla AMA, Kan JA, Patel NP, Yowler CJ, Claridge JA. Validation of Surgical Intensive Care-Infection Registry: a medical informatics system for intensive care unit research, quality of care improvement, and daily patient care. J Am Coll Surg 2008; 207 (02) 164-173
  • 12 Nelson SD, Del Fiol G, Hanseler H, Crouch BI, Cummins MR. A case report of refining user requirements for a health information exchange dashboard. Appl Clin Inform 2016; 7 (01) 22-32
  • 13 Kitson A, Harvey G, McCormack B. Enabling the implementation of evidence based practice: a conceptual framework. Qual Health Care 1998; 7 (03) 149-158
  • 14 Helfrich CD, Damschroder LJ, Hagedorn HJ. et al. A critical synthesis of literature on the promoting action on research implementation in health services (PARIHS) framework. Implement Sci 2010; 5 (01) 82
  • 15 Kellum JA, Lameire N, Aspelin P. et al. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl 2012; 2 (01) 1-138
  • 16 Kucukarslan SN, Corpus K, Mehta N. et al. Evaluation of a dedicated pharmacist staffing model in the medical intensive care unit. Hosp Pharm 2013; 48 (11) 922-930
  • 17 Zhang C, Zhang L, Huang L, Luo R, Wen J. Clinical pharmacists on medical care of pediatric inpatients: a single-center randomized controlled trial. PLoS One 2012; 7 (01) e30856
  • 18 Renaudin P, Boyer L, Esteve MA, Bertault-Peres P, Auquier P, Honore S. Do pharmacist-led medication reviews in hospitals help reduce hospital readmissions? A systematic review and meta-analysis. Br J Clin Pharmacol 2016; 82 (06) 1660-1673
  • 19 Hakkarainen KM, Gyllensten H, Jönsson AK, Andersson Sundell K, Petzold M, Hägg S. Prevalence, nature and potential preventability of adverse drug events - a population-based medical record study of 4970 adults. Br J Clin Pharmacol 2014; 78 (01) 170-183
  • 20 Wolfe D, Yazdi F, Kanji S. et al. Incidence, causes, and consequences of preventable adverse drug reactions occurring in inpatients: a systematic review of systematic reviews. PLoS One 2018; 13 (10) e0205426
  • 21 Feng C, Le D, McCoy AB. Using electronic health records to identify adverse drug events in ambulatory care: a systematic review. Appl Clin Inform 2019; 10 (01) 123-128
  • 22 Ibáñez-Garcia S, Rodriguez-Gonzalez C, Escudero-Vilaplana V. et al. Development and evaluation of a clinical decision support system to improve medication safety. Appl Clin Inform 2019; 10 (03) 513-520
  • 23 Hincapie AL, Alamer A, Sears J, Warholak TL, Goins S, Weinstein SD. A quantitative and qualitative analysis of electronic prescribing incidents reported by community pharmacists. Appl Clin Inform 2019; 10 (03) 387-394
  • 24 Roshanov PS, Fernandes N, Wilczynski JM. et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ 2013; 346 (7899): f657
  • 25 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
  • 26 Varghese J, Kleine M, Gessner SI, Sandmann S, Dugas M. Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. J Am Med Inform Assoc 2018; 25 (05) 593-602
  • 27 O'Sullivan D, Fraccaro P, Carson E, Weller P. Decision time for clinical decision support systems. Clin Med (Lond) 2014; 14 (04) 338-341
  • 28 Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform 2016; (Suppl. 01) S103-S116
  • 29 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007; 26-30
  • 30 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
  • 31 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13 (02) 138-147
  • 32 Hsieh TC, Kuperman GJ, Jaggi T. et al. Characteristics and consequences of drug allergy alert overrides in a computerized physician order entry system. J Am Med Inform Assoc 2004; 11 (06) 482-491
  • 33 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
  • 34 Hardigan PC, Popovici I, Carvajal MJ. Response rate, response time, and economic costs of survey research: a randomized trial of practicing pharmacists. Res Social Adm Pharm 2016; 12 (01) 141-148