V3I7P24

Management Information Systems as Strategic Catalysts for United States Healthcare Transformation: An Integrated Empirical Framework Spanning EHR Interoperability, Artificial Intelligence Decision Support, Predictive Population Analytics, Telehealth Equity, and Cybersecurity Governance

Akinola O. Adio^1*

Abstract

Background and Significance: The United States healthcare system spent $4.9 trillion in 2023, representing 17.6% of gross domestic product, the highest per-capita expenditure of any nation on earth. Yet outcomes betray this investment: U.S. life expectancy ranks 43rd globally, chronic disease burden exceeds every OECD peer, and an estimated $760 billion to $935 billion in annual spending is consumed by waste. This paper argues that the chasm between spending and outcome is fundamentally an information systems failure, and that Management Information Systems (MIS) represent the primary, scalable, evidence-supported lever for closing it. We examine five MIS domains transforming U.S. healthcare: Electronic Health Record (EHR) interoperability, Artificial Intelligence-driven Clinical Decision Support (AI-CDSS), predictive analytics and population health management, telehealth and remote patient monitoring, and cybersecurity governance.

Methods: We conducted a systematic integrative review across six academic databases, three U.S. government repositories, and four authoritative industry institutions, yielding 47 sources of High or Moderate quality published between 2019 and 2025. We introduce the Healthcare MIS Impact Architecture (HMIA), an original three-pillar theoretical framework synthesizing Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology behavioral foundations, healthcare information value chain theory, and Triple Aim outcome measurement.

Results: AI-CDSS reduces cost per unit of clinical outcome by 62% while improving patient outcomes 30 to 50% relative to standard care. EHR interoperability failures generate $45 billion to $130 billion in annual avoidable costs through duplicate testing, medication errors, and preventable readmissions. Healthcare cybersecurity breaches average $7.42 million per incident, the highest of any industry for fourteen consecutive years, with 2024 ransomware losses exceeding $21.9 billion. Telehealth has expanded rural care access but persists as a digital equity paradox: the populations with greatest need face the greatest engagement barriers. Predictive analytics drives 15 to 20% reductions in readmission rates when embedded in care management workflows. A National Healthcare MIS Value Model projects $270 billion to $795 billion in annual addressable savings from comprehensive MIS ecosystem transformation.

U.S. NATIONAL IMPACT: The moderate scenario savings projection of $530 billion annually, if realized, would: reduce per-capita healthcare spending toward OECD median levels; extend the Medicare Hospital Insurance Trust Fund solvency by an estimated 7 to 12 years; reduce employer-sponsored premium growth; and enable reallocation of resources toward the prevention, workforce development, and health equity programs that current evidence identifies as highest-return healthcare investments.

Conclusions: MIS is not a support function in U.S. healthcare. It is the primary infrastructure of clinical quality, operational efficiency, and national health equity. This paper advances an original theoretical framework, a twelve-question research agenda, and a federal policy platform grounded in the evidence base that the transformation demands.

Keywords:

Electronic Health Records, EHR Interoperability, AI Clinical Decision Support, Predictive Analytics, Population Health, Telehealth, Remote Patient Monitoring, Healthcare Cybersecurity, HIPAA, Digital Health Equity, TAM, UTAUT, MIS, U.S. Healthcare, Value-Based Care, Digital Transformation