MPA | Pharmacy News

Charles T. Makowski, Pharm.D., BCPS, pharmacy specialist, Henry Ford Health System, Detroit and Martin Ratusznik, B.S. Pharm, manager, Henry Ford Health System, Detroit

United States (U.S.) Healthcare System
In the United States (U.S.), improvements in population disease burden and access to quality health care lag behind that of other socioeconomically wealthy nations1,2, despite the extent of its healthcare expenditures, including rapidly rising drug costs.3, 4 This climate has contributed to a shift toward value-based healthcare delivery, which strongly implicates a healthcare organization’s ability to implement healthcare information technology (HIT) and to mature its adoption of healthcare analytics. While regulatory and economic incentives have helped to rapidly drive widespread adoption of HIT5, it is unclear how effectively U.S. health systems are using healthcare data to transform healthcare delivery and affect patient outcomes.6, 7

ASHP and MSHP have emphasized the strategic importance to health system pharmacy of automating the measurement and demonstration of pharmacy interventions, outcomes and value.8–10 However, national ASHP surveys indicate low self-reported rates of implementation of automated pharmacy intervention measurement (less than 20% in 2011) and slow expansion (about 33% in 2017). To illustrate some of the successes and barriers with implementing analytics-related recommendations from ASHP and MSHP, this case study aims to describe a Michigan health system’s experience developing, implementing, and governing an inpatient pharmacy metrics dashboard for achieving strategic pharmacy goals.

Henry Ford Health System
Henry Ford Health System (HFHS) is a six-hospital, 2300-bed system located within Southeastern Michigan and outlying areas. The HFHS Clinical Pharmacy Service Line consists of 360 inpatient full-time equivalent positions covering acute care, ambulatory care and clinical shared services. Its analytics personnel includes one FTE-dedicated data scientist and one mixed drug information pharmacist/analyst position. Notable standardized HIT systems at HFHS include Epic electronic medical record with Microsoft SQL Server platform and Microsoft Power (Desktop and Report Server) for automated on-premises reporting and dashboards. Policies and practices governing medication use within HFHS are developed and approved through a system multidisciplinary Medication Management Committee and its six Formulary and Drug Specialty Subcommittees. HFHS Clinical Pharmacy Service Line relationship with HFHS Enterprise Analytics and Data Warehousing is at the project collaboration level.

Governance Structure
In October 2018, as part of its biennial strategic planning, the HFHS Clinical Pharmacy Service Line conducted a guided Strategic Planning Session amongst its pharmacy leadership, which utilized HFHS Mission/Vision/Values as a framework for developing five goals with underlying strategies and quality improvement (QI) plans for 2019–2020. See Table 1 for basic description of steps involved with creation of the inpatient pharmacy dashboards. Initially, only dashboard operational owners were provided dashboard access as a data acclimation period, followed by provision of broader access once dashboard owners were comfortable navigating the dashboards and interpreting their data.

Table 1. Steps involved with development and implementation of inpatient pharmacy dashboards.

HFHS Clinical Pharmacy Service Line Dashboards
The Strategic Planning Session ultimately yielded a 2019–2020 QI plan with several pharmacy service domains for dashboards, as shown in Table 2. All dashboard measures were attributed at the hospital and patient department levels (e.g., warfarin education based on discharge department/location), thus serving multiple valuable functions (e.g., identify outliers, prioritize QI activities, mutual validation of dashboards and stakeholders’ anecdotal experience). Various other factors appropriate to the respective dashboard were also available for user manipulation or observation (e.g., encounter type filters for areas targeting emergency department admissions, dispensing matrix showing of the dashboard measures at the medication level).

Table 2. Inpatient pharmacy dashboard descriptions, quality measures and results.

AKI: acute kidney injury. HFHS: Henry Ford Health System. INR: international normalized ratio.

Lessons Learned
Key themes that emerged in the year after implementing the inpatient pharmacy dashboards revolved around data-to-decision latency, accountability and HIT/data governance. Some dashboard owners required several meetings to validate, understand and apply dashboard content within the established QI structure. Participants with research/QI experience and the responsible data analyst’s presence is strongly recommended to support strategic data integration. Organizations with more mature adoption of data analytics tend to centralize analytics and data warehousing services and resources, which may challenge reliable pharmacy access to necessary resources.13 Pharmacy technical acumen is essential to navigating the negotiations that may become necessary to obtain access to appropriate and secure data tools/platforms within the organization’s HIT governance framework.

In conclusion, strategic pharmacy operations benefit from incorporating informatics/analytics into its strategic planning, but requires careful planning and stakeholder engagement to be successful. Pharmacy engagement in corporate HIT governance and technical know-how are essential to establishing and sustaining reliable infrastructure for effective automatable QI activities.

References:
1. GBD 2016 Healthcare Access and Quality Collaborators. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016. Lancet. 2018 Jun 2;391(10136):2236-2271.
2. Murray CJ, Atkinson C, Bhalla K, et al. The state of US health, 1990-2010: burden of diseases, injuries and risk factors. JAMA. 2013 Aug. 14;310(6):591-608).
3. American Health Care: Rethinking the Challenges, Opportunities and Possibilities. Committee for a Responsible Federal Budget. American Health Care: Health Spending and the Federal Budget. 2018 May 16. Available from: http://www.crfb.org/papers/american-health-care-health-spending-and-federal-budget. Accessed 2019 Nov. 15.
4. Hartman M, Martin AB, Espinosa N, et al. National Health Care Spending In 2016: Spending And Enrollment Growth Slow After Initial Coverage Expansions. Health Aff (Millwood). 2018 Jan;37(1):150-160.
5. Office of the National Coordinator for Health Information Technology. 2015 Edition Market Readiness for Hospitals and Clinicians, Health IT Quick-Stat #55. Available from: https://dashboard.healthit.gov/quickstats/pages/2015-edition-market-readiness-hospitals-clinicians.php. March 2019. Accessed 2019 Nov. 17.
6. American Hospital Association, Data & Insights. AHA Annual Survey Database. Available from: https://www.ahadata.com/hospitals/. Accessed 2019 Nov. 20.
7. Office of the National Coordinator for Health Information Technology. Available from: https://dashboard.healthit.gov/evaluations/library.php. Accessed 2019 Nov. 18.
8. Vermeulen LC, Eddington ND, Gourdine MA, et al. ASHP Foundation Pharmacy Forecast 2019: Strategic Planning Advice for Pharmacy Departments in Hospitals and Health Systems. Am J Health Syst Pharm. 2019 Jan. 16;76(2):71-100.
9. The consensus of the Pharmacy Practice Model Summit. Am J Health Syst Pharm. 2011 Jun 15;68(12):1148-52.
10. Bickel RJ, Collins CD, Lucarotti RL, et al. Moving the Pharmacy Practice Model Initiative forward within a state affiliate. Am J Health Syst Pharm. 2014 Oct. 1;71(19):1679-85.
11. American Society of Health-System Pharmacists. ASHP Practice Advancement Initiative. Available from: http://www.ashpmedia.org/pai/docs/PAI-Progress-Measures.pdf. Accessed 2019 Nov. 16.
12. Andrawis M, Ellison LTCC, Riddle S, et al. Recommended quality measures for health-system pharmacy: 2019 update from the Pharmacy Accountability Measures Work Group. Am J Health Syst Pharm. 2019 Jun 3;76(12):874-887.
13. Healthcare Information and Management Systems Society. HIMSS Analytics. Analytics Adoption Model for Analytics Maturity (AMAM). Available from: https://www.himssanalytics.org/amam. Accessed 2019 Nov. 12

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