Executive summary of the meeting of the 2020 ashp Commission on goals: preparing the healthcare workforce for a digital future

clinical decision support systems, Commission on Goals, data science, digital health, precision medicine, workforce

Joseph A. Oddis Global Headquarters of ASHP

Bethesda, MD

March 3, 2020

Address correspondence to [email protected].

Background

Advances in healthcare technology such as digital therapeutics, personalized medicine, virtual health, and robotics are rapidly changing how healthcare is practiced and delivered. Increased data access and sharing via digital solutions can prioritize personalization and enhance the patient experience. With the rapid introduction of these products and services for use by clinicians and patients, health systems will need to embrace new business, care delivery, and risk models to create affordable, high-quality healthcare solutions. Healthcare providers will be called on to create and implement those new models, which will require a fundamental shift in the ways they learn, train, and practice. To fulfill its mission, ASHP will need to provide the support the pharmacy workforce needs to make these changes, from education to research and from advocacy to providing essential resources.

Overview

Each year ASHP convenes a Commission on Goals, composed of thought leaders in healthcare and related fields, to review societal and healthcare trends and developments that may affect ASHP members and the patients they serve and to provide guidance to the ASHP Board of Directors about potential strategic areas of focus for ASHP. The March 3, 2020, Commission meeting focused on strategies to best prepare the healthcare workforce to optimize patient-centered care and medication use in a digital future. Members of the Commission ( appendix) were selected from key leaders in pharmacy, medicine, nursing, information technology, other healthcare associations, and academia for their unique ability to discuss the digital future of healthcare and strategies to best prepare the healthcare workforce to optimize patient-centered care and medication use in that digital future. This publication is intended as a general overview of the discussion among participants and does not represent the official position of any of the individuals or organizations involved.

Improving care through advances in healthcare technologies.

Members of the Commission agreed that the healthcare sector often lags other sectors in technological innovation. One reason is that the “fail fast” approach to technology is not compatible with patient care, in which failure isn’t accepted and actions are based on carefully weighing all available evidence. Another is healthcare’s fragmented approach to budgeting, which creates multiple, often competing agendas that discourage the unified mission that an innovator can bring to a specific challenge. The Commission noted that although innovators from outside healthcare often focus on solving the problems of a particular type of provider or patient, they lack the information and sometimes the vision to tackle system-wide challenges, creating an opportunity for health systems to take on such initiatives, on their own or in cooperation with outside industries.

Commission members identified conditions that promote innovation. First, innovation is more likely to occur in areas where high-quality data is available. Commission members were quick to point out that high-quality data does not necessarily mean a lot of data; a small set of high-quality data can be more powerful than a larger set of less reliable data. Second, as the Commission also pointed out last year, adoption is more likely to occur in operational rather than clinical processes, especially where improved patient or financial outcomes can reliably be predicted. Finally, innovation is more likely when a mundane, repeatable task can be automated.

The Commission provided several examples of opportunities for operational innovation. Because of the large amount of data available and the obvious potential effects on patient and fiscal outcomes, it was suggested that drug inventories and supply chains would likely be more closely monitored in the future, with use of both traditional data analytics and artificial intelligence to identify optimal inventory and potential disruptions. The Internet of Things could be helpful in identifying expired or recalled drugs.

Learning from the experience of the electronic health record.

The Commission discussed at length the example of the implementation of the electronic health record (EHR). Documentation, alert fatigue, and EHR burden have been shown to contribute to clinician burnout. The promise of a single health record that would follow a patient to any care setting has been frustrated by many factors, foremost among them a lack of data interoperability. Some of the barriers to interoperability are by choice, as different providers and care settings sought EHR customizations to meet their individual needs. Another barrier is inherent in the choice of coding for the data (ie, use of clinical terminologies such as SNOMED CT, LOINC, and RxNorm in the EHR vs classification systems such as DRGs and CPT codes used in billing systems). One Commission member suggested that given the small number of EHR vendors, interoperability should be a relatively easy task, but the major challenge seems to be that each EHR vendor has its own design and there are no driving incentives to collaborate.

A growing barrier to efficient use of EHRs is the growing volume and types of data one system is being asked to store and provide intelligibly. Pharmacogenomics promises better, more individualized treatments, but storing and retrieving genetic data has presented challenges. One example provided was how to note in the EHR a patient’s HLA-B*57:01 genotype, which could be used to predict a hypersensitivity to abacavir. In the European Union, the Ubiquitous PGx (U-PGx) project was initiated in 2016 to address the need to screen for potential contraindications by implementing pharmacogenomic panel testing and clinical decision support (CDS) across 7 European countries.¹ In the U-PGx project, active, interruptive CDS alerts clinicians of relevant gene-drug interactions via a pop-up message in the EHR or e-prescription system at the time of prescribing. Passive CDS is delivered inside the EHR system as a digital report, or outside the EHR system via mobile- and paper-based solutions. Participating patients carry a Safety-Code card that provides clinicians who do not have access to the EHR or e-prescription system with Web access to the patient’s pharmacogenomic information via a QR code.

The Commission saw potential for progress in EHR use, however. There is hope that the mundane, repeatable task of entering data in EHR systems may be automated in the future via artificial intelligence,² and that patient care could be improved through predictive modeling based on EHR data.^3,4 Some Commission members saw potential progress in data interoperability in the work of communities such as the Observational Health Data Sciences and Informatics (OHDSI) program. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) transforms data contained in disparate databases (eg, those built on commonly used ontologies, such as SNOMED, LOINC, or RxNorm) into a common format (data model) and representation (terminologies, vocabularies, coding schemes), allowing integration and easier use.⁵ In a similar vein, the World Health Organization and the International Health Terminology Standards Development Organisation have been working on a similar project that will enable mapping between SNOMED CT and different versions of the International Classification of Diseases (ICD) coding scheme, as have the National Library of Medicine and the National Center for Health Statistics (through the Interactive Map-Assisted Generation of ICD Codes, or I-MAGIC, algorithm).⁶

Connected care.

The Commission discussed the increasing adoption of telehealth. According to a 2019 American Hospital Association report summarized at the meeting, 66% of health systems have a telehealth program for consultations and office visits, and 70% use telehealth for stroke care.⁷ Since the report was published in early 2019, the coronavirus disease 2019 (COVID-19) pandemic has dramatically increased use of telehealth, and it is likely that those numbers now understate use. The report also noted that 97% of patients surveyed were satisfied with their first telehealth experience and would recommend the program, begging the question why telehealth has not been more widely adopted.⁷ The Commission discussed several of the barriers to adoption mentioned in the report:

• Restrictions on how payers cover and pay for telehealth

• Licensure laws and other regulations that limit the ability to provide telehealth services across state lines

• Lack of adequate broadband service in some areas to support telehealth

• High cost of the technologies and infrastructure and a lack of funding

• Inadequate clinical engagement and readiness without consideration of human factors in the user experience and workflows for both clinicians and patients, as was discussed regarding EHR adoption

Members of the Commission acknowledged that inadequate training of healthcare providers is also a barrier and were informed that the Association of American Medical Colleges was preparing telehealth competencies.⁸

The Commission discussed a report on state telehealth Medicaid fee-for-service policies from the Center for Connected Health Policy that found that live video patient encounters are now reimbursed by all states and the District of Columbia, and that many states have added more medical specialties and services eligible for reimbursement.⁹ The Commission noted that the Center for Medicare & Medicaid Innovation has made progress in its Next Generation ACO Model Telehealth Expansion Waiver,¹⁰ but that much more needs to be done to reconcile current policy with the ambitious goals of the Affordable Care Act.¹¹ (Other aspects of connected care discussed by the Commission are explored in the section “Digital therapeutic products” below.)

Innovations in dispensing.

Despite the barriers mentioned above, technological innovations are taking place in the healthcare community, often by disruptive innovators from outside industries who can identify and fill a patient need. GoodRx has created a business model that competes with insurance company pharmacy benefit managers (PBMs) to provide customers with savings though a mobile phone app. PillPack has been widely recognized as fundamentally changing pharmacy dispensing by bundling and sorting prescriptions, freeing consumers from frequent trips to the pharmacy and the difficult chore of sorting and timing medications. The next generation of change can be seen in in-home medication dispensers from companies such as Spencer Health Solutions and Hero Health that take those services further. In addition to bundling, sorting, and reordering prescriptions, the dispensers are connected to apps that provide users with reminders to take medications and that share adherence data with healthcare providers and caregivers. The dispensers are also designed to collect other health information from users that can help providers evaluate the patient’s response to medication therapy and health status more generally (eg, through questions designed to detect adverse reactions). Members of the Commission suggested that it would not be far-fetched to imagine a similar system that would integrate data from a patient wearable for the same purposes. It was noted that the increase in the amount of patient data from these systems was accompanied by a loss of patient interaction with providers (eg, pharmacy visits), but that the data could be used for more targeted and important interventions, perhaps at the loss of more routine touch points.

The central role of data.

The Commission also discussed the issues of data quality, unity, and ownership. Precision medicine and pharmacogenomics will require an immense amount of data, not only on individual patients but on similar populations, to provide predictive capacity. Although progress is being made in collecting pharmacogenomic data in drug trials, the study populations are often insufficiently diverse to reliably predict use in many populations. In addition, many of the important long-range health and disease studies have focused on white, European-descendant males, which may limit their applicability.

The Commission also discussed the controversial issue of data ownership. The European Union and United States have taken different approaches to the issue, and the members of the Commission expressed concern that industry control of patient data could lead to abuses and stifle innovation. One Commission member pointed out that some in the tech industry are exploring the concept of “data dignity,” in which a person owns data about themselves and is empowered to control it.¹²

Optimizing medication use through technology.

The Commission reviewed the following definitions provided by the Digital Therapeutics Alliance:

• Digital health includes technologies, platforms, and systems that engage consumers for lifestyle, wellness, and health-related purposes; capture, store or transmit health data; and/or support life science and clinical operations. These products typically do not require clinical evidence or regulatory oversight.

• Digital medicine includes evidence-based software and/or hardware products that measure and/or intervene in the service of human health. Clinical evidence is required for all digital medicine products, and requirements for regulatory oversight vary.

• Digital therapeutic products deliver evidence-based therapeutic interventions to prevent, manage, or treat a medical disorder or disease. Clinical evidence and real-world outcomes are required for all digital therapeutic products. They are reviewed by regulatory bodies as required to support product claims of risk, efficacy, and intended use.¹³

Digital therapeutics is still in its infancy, but the Commission discussed several successful examples. Generally, digital therapeutic products are used to monitor indicators of a patient’s condition (eg, blood pressure [BP], hemoglobin A1c) or encourage behaviors (eg, adherence to medication or behavioral therapies). The Commission noted that the products generally share several similar features: a digital interface used by patients, clinicians, and sometimes medical devices; wearable devices that provide information about a patient’s conditions to patients, clinicians, or medical devices; integration of disparate sources of data; enhanced patient engagement with their data and treatment; and automated or live digital coaching features to improve patient adherence with medication and/or behavioral therapies. Commission members suggested that development and adoption of such products may be driven by employers seeking to reduce health insurance costs or by the insurance companies themselves. Omada (discussed in more detail below) has taken advantage of this market by constructing a business plan in which the company is paid when the population served achieves it goals.

Digital therapeutic products.

The Commission discussed several examples of digital therapeutic products. Livongo Health provides digital care programs for diabetes prevention and care, weight management, hypertension, and behavioral health. The programs use connected devices (eg, glucose meters, BP monitors, scales, and activity tracking devices) to share data with patients and providers and provide personalized expert coaching. A number of mobile apps to aid asthma care are available, including AsthmaMD, Asthma Storylines, Hailie, KagenAir, and Propeller Health. Some of these apps interface with electronic sensing devices attached to medication inhalers that transmit a signal via smartphone to secure cloud-based databases accessible in real time to patients and their caregivers, enabling clinicians to monitor individual patients and an entire clinic population simultaneously. Use of actuation sensors has been associated with lower overall costs for asthma care and better clinical outcomes.¹⁴ Some of these apps consolidate additional patient-entered data with outside data (eg, weather, pollen counts) to identify potential asthma attack triggers.

The Omada for Prevention program has demonstrated a significant reduction in risk for 3 chronic diseases: type 2 diabetes, stroke, and heart disease.¹⁵ Omada digital care programs provide human support to help people achieve their specific health goals through sustainable lifestyle change. The diabetes module connects with the Abbott FreeStyle Libre continuous glucose monitoring system. Partners Healthcare provides another example of the successful application of digital care programs. Their hypertension care program, which has a goal of removing hypertension care from the physician’s office entirely, depends on care navigators who collect data from patients’ wearable BP monitors to more quickly get BP under control.

Welldoc’s BlueStar is a Food and Drug Administration (FDA)–cleared digital health solution for patients with type 1 or type 2 diabetes that delivers feedback to help improve long-term health. BlueStar also integrates with blood glucose meters, pharmacies, laboratories, and activity and fitness trackers, as well as aggregating data for delivery to the care team.¹⁶

The Happify platform offers scientifically validated assessment tools to identify the activities (eg, 4-week programs on more than 60 topics, activities and games, guided meditation, social connections, and support) needed to build skills for better emotional health.¹⁷ Happify Health recently partnered with the American Heart Association (AHA) to develop and launch a digital mental health program that aims to reduce stress and encourage healthy behaviors among people with high BP and high cholesterol. The program consists of 10 4-week programs that teach users stress-reduction strategies and encourage regular activity and heart-healthy eating habits, with a focus on “Life’s Simple 7,” a collection of risk factors identified by AHA that can increase wellness and reduce cardiovascular deaths.¹⁸

Members of the Commission noted the challenges of products that collect and transmit patient data directly to a dosing device, removing the provider intermediary to make real-time dosing decisions. Artificial pancreas device systems,¹⁹ such as the Medtronic MiniMed 670G System, can automatically adjust basal insulin by continuously increasing, decreasing, or suspending delivery of insulin based on data from a continuous glucose monitor. The Open Artificial Pancreas System project is a collaborative dedicated to the mission of developing and disseminating technology that automatically adjusts an insulin pump’s basal insulin delivery to keep blood glucose in a safe range.²⁰ The Commission agreed that systems that directly act on patient data provided by wearable devices will become more common as consumer and provider confidence in them increase.

Digital ingestion tracking systems.

Another prominent example of advances in digital therapeutics is FDA’s approval of digital ingestion tracking systems, the first of which was Abilify MyCite.²¹ In such systems, a wearable device detects ingestion, signaling a patient’s cellphone through an app and transferring data to the care team. Proteus Digital Health has developed Proteus Discover,²² a system consisting of an ingestible sensor, a wearable sensor patch, an application on a mobile device, and a provider portal. The patient takes a medication with the ingestible sensor, which transmits a signal to the wearable patch when it reaches the stomach. A digital record is sent from the sensor to the patient’s mobile device and then to the Proteus cloud, where healthcare providers and caregivers access it. The system has been studied in the treatment of hypertension, hepatitis C, and tuberculosis, all conditions in which adherence to medication therapy is crucial. The system’s app allows patients to track their medications, steps, other activity, rest, heart rate, BP, and weight and to set multiple medication-taking schedules and receive medication reminders. Despite encouraging results, including a demonstration that the system could help healthcare providers determine which hypertension patients needed medication titration and those that needed adherence counseling, the company filed for bankruptcy in June 2020.²³ etectRx offers ID Cap, a similar system built around a hard gelatin capsule with an embedded ingestible wireless sensor that was approved by FDA in December 2019.²⁴

Predictive platforms.

Novartis, Chugai, Brigham and Women’s Hospital, and Mayo Clinic use the FDA-cleared predictive Biovitals Analytics Engine from Biofourmis. The platform receives physiologic data in near real time from FDA-cleared sensors and uses artificial intelligence and machine learning to identify correlations between multiple vital signs and the patient’s daily activities, constructing an individualized biometric signature. The system can alert providers to changes in patients’ measured vital signs from baseline, allowing cli-nicians to respond before a serious event. The platform’s performance has been validated in clinical studies involving monitoring of patients with complex chronic conditions, including cardiovascular conditions (heart failure, coronary artery disease, and atrial fibrillation/obstructive sleep apnea), respiratory conditions (asthma and chronic obstructive pulmonary disease), cancer (solid tumors), and acute and chronic pain.²⁵

From small devices to big data.

The Commission discussed the example of AIR Louisville, a collaborative study of asthma conducted in a real-world setting.²⁶ The researchers deployed inhaler sensors and a digital health platform to monitor where and when patients used inhaled medications for asthma, assessed the environmental conditions that might have influenced asthma symptoms, and shared those findings with participants and city decision-makers. Participants experienced positive clinical outcomes, including a 78% reduction in rescue inhaler use and a 48% improvement in symptom-free days. The crowd-sourced data on inhaler use, combined with environmental data, was used by municipal policy-makers to develop a community asthma notification system and to inform their decisions on tree canopy and removal, zoning for air pollution emission buffers, and recommended truck routes. The Commission envisioned the power similar studies could have if coupled with the emerging science of artificial intelligence.

Regulation.

One of the many issues presented by the explosion of digital health apps is their regulation. The FDA Software Precertification (Pre-Cert) Pilot Program, as outlined in FDA’s Digital Health Innovation Action Plan,²⁷ has set a goal of approving 6 or 7 software-based medical devices each week. Members of the Commission were heartened that the program would focus on manufacturers that “have demonstrated a robust culture of quality and organizational excellence” and “are committed to monitoring real-world performance of their products” after they reach the market. However, even if FDA does not reach its ambitious goals, the number of apps in use by patients could quickly outstrip the healthcare system’s ability to meaningfully engage with patients using them. This gap could create a role for health systems in developing and managing “health app formularies” to provide a basis for mutually beneficial use.

Nongovernmental actors have also stepped in to provide guidance on development and use of health apps. The Mobile App Rating Scale (MARS) created at Queensland University of Technology uses a 5-point Likert scale to score 4 well-defined app categories: user engagement, functionality, esthetics, and information accuracy.²⁸ The Digital Therapeutic Alliance, an industry collaboration, has provided 10 guiding principles²⁹ and a set of product best practices.³⁰ Xcertia, a multidisciplinary group of health information technology and medical organizations, has developed a voluntary set of guidelines for safe and effective app use focused on focus on 5 key areas: operability, privacy, security, app content, and usability.³¹

Challenges to optimizing medication use through technology.

Commission members observed that current medication-use practices assume that for most medications, use is optimal as prescribed. With a few notable exceptions, our healthcare system disincentivizes adjustment to therapy. Commission members agreed that in an ideal world, the results of medication therapy would be measurable, and that changes would rapidly be made to achieve the best outcomes. Commission members noted the progress exemplified by automated pancreas systems and digital programs to control hypertension but noted the challenges to such models. Lack of physiologic markers to many conditions, and the difficulty of measuring those that exist, limit the applicability of such models. Another challenge is the lack of data sets to establish appropriate markers, which Commission members agreed should be a priority.

One of the challenges of personalized medicine is precision dosing. FDA has evaluated 181 drugs approved between 2013 and 2017 and determined that about half may be suitable for precision dosing, but unfortunately the labeling for 61% of the candidate drugs lacked the titration information needed to develop precise dosages.³² One Commission member pointed out that based on available data, it would be possible to implement precision dosing for 10 to 12 drugs. Some Commission members envisioned a day when dosing calculations would be performed by machine algorithms, noting that clinicians are already comfortable with CDS providing advice and would become more comfortable with machine-calculated dosing over time. It was suggested that a change in attitude may be required; if we devote time and energy to developing CDS advice and alerts, the result will be more advice and alerts. More effort may need to be devoted to dosing algorithms for such an effort to succeed.

Precision dosing will depend in part on the availability of pharmacogenomic data. Groups such as the Global Alliance for Genomics and Health (GA4GH) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) are leading efforts to develop and share such data. GA4GH is a policy-framing and technical standards–setting organization seeking to enable responsible genomic data sharing, and CPIC’s mission is facilitating use of pharmacogenetic tests for patient care.

Preparing the workforce for the digital future

Preparing the healthcare workforce for the impending digital future is a daunting task. There is widespread agreement that it is impossible to predict what skills and competencies will be required even in the relatively short term of 5 to 10 years, let alone the 30- to 40-year careers of graduates.

The Commission discussed several different efforts on this front. AHA convened its Changing Workforce Task Force in 2019 to provide strategic thought leadership on the future of the healthcare workforce and met through 2020 to analyze existing research, develop public policy proposals, identify leading field practices, and produce education materials and resources.³³ The Accreditation Council for Graduate Medical Education has been examining the issue for at least 5 years.^34,35

Commission members agreed that preparing for this uncertainty by teaching care providers how to adapt over the course of a career to rapid changes will be essential. One Commission member provided 4 lessons learned from industry outside healthcare. First, traditional teaching and training models are being disrupted by online models (eg, Coursera) that provide on-demand learning; by self-organizing interest or learning networks providing information, networking, and informal learning opportunities; and by events such as competitions and “hack-a-thons” that spur individual and group learning. Second, the different kinds of learning taking place will require a reexamination of current credentialing models. The testing model used in much of current credentialing will not only have to evolve much more quickly to keep pace with changes in practice, but different forms of credentialing will need to be developed to reflect the new learning models. Simulation centers presenting real-life scenarios could, for example, replace or supplement testing. Commission members expressed a hope that new credentialing models and technologies would enable clinicians to demonstrate competence rather than just knowledge. Third, interprofessional practice will require interprofessional models of study and credentialing. Although joint accreditation of healthcare provider education is well underway and valued by providers and their employers, it will have to be greatly expanded to meet the future needs of healthcare.³⁶ Finally, healthcare providers will need to embrace reverse mentoring, in which established providers seek out opportunities to learn from younger colleagues and more recent graduates, whose education could provide a more contemporary view.

The Commission recognized the burden of continued learning and adaptation and suggested that employer expectations may need to evolve as well. Healthcare providers may need to take sabbaticals to learn new skills, and the grueling schedules of many healthcare providers may need to be altered to provide time for substantive and meaningful education. Commission members also suggested that there are subjects (eg, information and data sciences, genomics, leadership skills) that healthcare providers should study throughout the course of their education and careers. Several Commission members asked why healthcare educators do not recruit individuals with different backgrounds (eg, informatics) that would fill gaps in healthcare professions.

Conclusion

The Commission on Goals: Preparing the Healthcare Workforce for a Digital Future identified important trends in the development and use of healthcare technology, explored some of the challenges confronting health systems and healthcare providers in preparing for the changes those technologies will bring, and outlined potential opportunities for ASHP and other healthcare-related associations.

Appendix—Roster of 2020 ASHP Commission on Goals: Preparing the Healthcare Workforce for a Digital Future

John A. Armitstead, MS, RPh, FASHP

Commission Chair

System Director of Pharmacy

Lee Health

Fort Myers, FL

Kelly M. Smith, PharmD, FASHP, FCCP

Commission Vice Chair

Dean and Professor

University of Georgia College of Pharmacy

Athens, GA

Paul W. Abramowitz, PharmD, ScD (Hon), FASHP

Chief Executive Officer

American Society of Health-System Pharmacists

Bethesda, MD

Kasey K. Thompson, PharmD, MS, MBA

Commission Secretary

Chief Operating Officer & Senior Vice President

American Society of Health-System Pharmacists

Bethesda, MD

Commission Participants

Peter Angood, MD, CPE, FRCS(C), FACS, MCCM

Chief Executive Officer and President

American Association for Physician Leadership

Tampa, FL

Oriana Beaudet, DNP, RN, PHN

Vice President of Nursing Innovation American Nurses Association

Silver Spring, MD

Megan Coder, PharmD, MBA

Executive Director

Digital Therapeutics Alliance

Arlington, VA

Anupam DattaMajumdar, PhD

Vice President, Advanced Technologies, Research and Data Science

Omnicell Inc.

Mountain View, CA

Allen Flynn, PharmD, PhD

Assistant Professor of Informatics for Learning Health Systems

University of Michigan Medical School

Ann Arbor, MI

William J. Gordon, MD

Medical Director, Health Innovation Platform

Partners HealthCare System

Brigham and Women’s Hospital

Boston, MA

Mira Irons, MD

Chief Health and Science Officer

American Medical Association

Chicago, IL

Thomas J. Johnson, PharmD, MBA, BCCCP, BCPS, FASHP, FCCM

ASHP President-Elect

Assistant Vice President – Hospital Pharmacy, Avera Health

Avera McKennan Hospital

Sioux Falls, SD

Christene M. Jolowsky, BSPharm, MS, FASHP

ASHP Treasurer

Senior Director of Pharmacy, Hennepin Healthcare System, Minneapolis

Hennepin Healthcare

Minneapolis, MN

Joy A. Lewis, MPH, MSW

Vice President, Strategic Policy Planning

American Hospital Association

Washington, DC

COL Mark Maneval, PhD

Senior Data Scientist and Chief, Pharmacy Clinical Decision Support

Enterprise Intelligence and Data Solutions

Defense Health Agency

San Antonio, TX

Rosha Champion McCoy, MD, FAAP

Senior Director, Advancing Clinical Leadership and Quality

Health Care Affairs

Association of American Medical Colleges

Washington, DC

Tina Moen, PharmD

Senior Deputy Chief Health Officer and Chief Pharmacy Officer

IBM Watson Health

Greenwood Village, CO

Kathleen S. Pawlicki, BSPharm, MS, RPh, FASHP

ASHP President

Vice President and Chief Pharmacist, Beaumont Health

Royal Oak, MI

Prad Prasoon, BE

Emerging Businesses, Ventures & Technology Strategies Director

American Heart Association

Dallas, TX

Dimitra V. Travlos, PharmD

Assistant Executive Director and Director, Continuing Pharmacy Education Provider Accreditation

Accreditation Council for Pharmacy Education

Joint Accreditation for Interprofessional Continuing Education

Chicago, IL

Acknowledgments

Bruce Hawkins, BS, BA, is acknowledged for drafting this summary.

Disclosures

The author has declared no potential conflicts of interest.