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Sotto-Santiago S, Neal C, Caudill D, Gist A, Eastwick S, Palmer MM, Geraci MW, Aronoff DM. Business of Medicine: Developing Leaders in Academic Medicine and Learning Health Systems. J Healthc Leadersh 2024; 16:227-233. [PMID: 38946732 PMCID: PMC11212659 DOI: 10.2147/jhl.s463206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024] Open
Abstract
Purpose To develop healthcare professionals as clinical leaders in academic medicine and learning health system; and uncover organizational barriers, as well as pathways and practices to facilitate career growth and professional fulfillment. Methods The Department of Medicine strategic plan efforts prompted the development of a business of medicine program informed by a needs assessment and realignment between academic departments and the healthcare system. The business of medicine leadership program launched in 2017. This descriptive case study presents its 5th year evaluation. Competencies were included from the Physician MBA program and from specific departmental needs and goals. Results The program hosted a total of 102 clinical faculty. We had a 37% response rate of those retained at Indiana University School of Medicine. Overall, responses conveyed a positive experience in the course. Over 80% of participants felt that they gained skills in professional reflection, professional socialization, goal orientation, critical thinking, and commitment to profession. Financial literacy was overwhelmingly the skill that was reported to be the most valuable. Finance and accounting were mentioned as the most difficult concepts to understand. Familiar concepts included communication, LEAN, and wellness related topics. One hundred percent of participants said they are utilizing the skills gained in this program in their current role and that they would recommend the course to others. Conclusion Business of medicine courses are more common now with programs describing elements informed by health system operations. However, few programs incorporate aspects of wellness, equity, diversity, inclusion, and health equity. Our program makes the case for multiple ways to develop inclusive leaders through a focused five-month program. It also recognizes that to really impact the learning health system, health professionals need leadership development and leaders suited to work alongside career administrators, all aiming towards a common goal of equitable patient-centered care.
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Affiliation(s)
- Sylk Sotto-Santiago
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chemen Neal
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Darren Caudill
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Amanda Gist
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Susannah Eastwick
- Kelley School of Business, Indiana University, Indianapolis, IN, USA
| | - Megan M Palmer
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mark W Geraci
- Department of Medicine, University of Pittsburgh Health Sciences, Pittsburgh, PA, USA
| | - David M Aronoff
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
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Welch LC, Brewer SK, Schleyer T, Daudelin D, Paranal R, Hunt JD, Dozier AM, Perry A, Cabrera AB, Gatto CL. Learning health system benefits: Development and initial validation of a framework. Learn Health Syst 2024; 8:e10380. [PMID: 38249854 PMCID: PMC10797574 DOI: 10.1002/lrh2.10380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/14/2023] [Accepted: 06/22/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers. Engaging these stakeholders requires communicating clear, tangible value propositions. Existing models identify broad categories of benefits but do not explicate the full range of benefits or ways they can manifest in different organizations. Methods To develop such a framework, a working group with representatives from six Clinical and Translational Science Award (CTSA) hubs reviewed existing literature on LHS characteristics, models, and goals; solicited expert input; and applied the framework to their local LHS experiences. Results The Framework of LHS Benefits includes six categories of benefits (quality, safety, equity, patient satisfaction, reputation, and value) relevant for a range of stakeholders and defines key concepts within each benefit. Applying the framework to five LHS case examples indicated preliminary face validity across varied LHS approaches and revealed three dimensions in which the framework is relevant: defining goals of individual LHS projects, facilitating collaboration based on shared values, and establishing guiding tenets of an LHS program or mission. Conclusion The framework can be used to communicate the value of an LHS to different stakeholders across varied contexts and purposes, and to identify future organizational priorities. Further validation will contribute to the framework's evolution and support its potential to inform the development of tools to evaluate LHS impact.
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Affiliation(s)
- Lisa C. Welch
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Sarah K. Brewer
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Titus Schleyer
- Center for Biomedical InformaticsRegenstrief InstituteIndianapolisIndianaUSA
- Indiana Clinical and Translational Sciences InstituteIndiana UniversityIndianapolisIndianaUSA
| | - Denise Daudelin
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Rechelle Paranal
- South Carolina Clinical and Translational Research Institute, Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Joe D. Hunt
- Indiana Clinical and Translational Sciences InstituteIndiana UniversityIndianapolisIndianaUSA
| | - Ann M. Dozier
- University of Rochester Clinical and Translational Science Institute, University of RochesterRochesterNew YorkUSA
| | - Anna Perry
- Wake Forest Clinical and Translational Science Institute, Wake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Alyssa B. Cabrera
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Cheryl L. Gatto
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical CenterNashvilleTennesseeUSA
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Balch JA, Ruppert MM, Loftus TJ, Guan Z, Ren Y, Upchurch GR, Ozrazgat-Baslanti T, Rashidi P, Bihorac A. Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review. JMIR Med Inform 2023; 11:e48297. [PMID: 37646309 PMCID: PMC10468818 DOI: 10.2196/48297] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/17/2023] [Indexed: 09/01/2023] Open
Abstract
Background Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Matthew M Ruppert
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Ziyuan Guan
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Yuanfang Ren
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
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ASPIRE: A Program for Developing Clinician Educators' Scholarship, Advancement, and Sense of Community. J Gen Intern Med 2022; 37:1953-1962. [PMID: 35083648 PMCID: PMC9198196 DOI: 10.1007/s11606-021-07243-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Faculty development programs encouraging clinician educators' scholarship have been established at many medical schools. The same is true for programs that address the isolation and loneliness many faculty members feel in their day-to-day clinical work and administration. Few programs have explicitly combined development of scholarship and sense of community. AIM The goals of the Advanced Scholars Program for Internists in Research and Education (ASPIRE) are as follows: (1) provide training in scholarship development including research methods, implementation, and dissemination; (2) provide expert mentoring and support for professional development; and (3) create a greater sense of campus community. SETTING ASPIRE scholars are clinician educators in the Department of Medicine at Indiana University School of Medicine. PROGRAM DESCRIPTION The program runs 18 months, includes intensive mentoring, covered time for scholars and mentors, resources, and two half-day educational sessions per month focused on scholarship and community development. PROGRAM EVALUATION Institutional leaders' public statements and actions regarding ASPIRE were documented by program leadership. Data collected from ASPIRE mentors and scholars through interviews and free text survey responses were analyzed using an immersion/crystallization approach. Two central themes were identified for both scholars and mentors: benefits and challenges of the program. Benefits included mentors, program design, community development, increased confidence, skills development, improved patient care, and institutional impact. Challenges included time to accomplish the program, balance of community-building and skills development, and lack of a clear path post-ASPIRE. DISCUSSION Combining skills-based learning with safe psychological space were judged important elements of success for the ASPIRE program. Conversations are ongoing to identify opportunities for scholars who have completed the program to continue to pursue scholarship, expand their skills, and build community. We conclude that the program both is feasible and was well-received. Sustainability and generalizability are important next steps in ensuring the viability of the program.
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