How the Analytics of Care Can Balance Workforce Capacity - MedCity News (2024)

U.S. healthcare is experiencing a supply and demand crisis as it races to keep pace with an aging population amidst a workforce shortage and mounting financial pressures. And the situation appears unlikely to improve anytime soon. In fact, recent projections anticipate a shortfall of 139,000 physicians in the next decade, according to the Association of American Medical Colleges.

One limiting factor preventing the efficient management of workforce resources is a lack of data interoperability.

Disparate datasets – emitting from legacy EMRs, practice management systems and third-party software – have created operational blind spots preventing health systems from precisely managing the allocation of care. And instead of a holistic view, across an ecosystem of care, health systems piecemeal fragmented insights against limited resources.

Data linkages unlock health system capacity

How do we go from myopic to digitally aware? The answer lies in becoming a real-time health system able to achieve the awareness to orchestrate how, when, and where care is accessed. It’s about having the controls to dynamically manage clinics, hospitals, doctors, and system-wide resources, matched against patient needs and capacity – instantly. Only then, can we divert traffic from emergency rooms, route patients to clinics and virtual care, and make time for doctors to treat our aging population.

A transition from a disjointed view of operations to a predictive model starts with data unification.

The advent of generative AI and machine learning models, in a blink, can ingest, learn, and organize complex, massive datasets. As a result, health systems can operate with the intelligence to inform how to balance workforce resources, to control costs, and to free up capacity.

The always-on, connected health system is not a distant moonshot, but today’s reality. Pragmatic applications of AI can achieve the data fidelity – and clarity – to understand how care is accessed, where needs are growing, and to best match resources to ensure quality outcomes.

The reality is not every patient needs to see a doctor. It’s about matching the right care, at the right time, at the right location that extends resources and realigns how people and services are distributed.

Leveraging real-time data for a new model of care

Data intelligence is no longer a luxury, but an operational imperative to manage an entire portfolio of care. And in healthcare – an industry beset by data isolation – the analytics of care delivery is more important than ever.

Take, for example, a health system that is working to balance the demand for ambulatory care across providers, nurses, retail, and telehealth. To do so, aggregation of cross-functional data, including capacity and utilization, is essential to enable a self-scheduling experience that both increases choice, and has the real-time logic to route patients to the best venues of care.

The unification of data isn’t just about convenience and capacity—it’s about creating a better model of care. The traditional “find-a-doc” features fall short of connecting patients to the right care, at the right time. Achieving data intelligence enables us to move to a “find care” model that shields providers – creating time to treat pressing, chronic conditions – and delivers the operational controls to predict and auto-heal the demand for care against fluctuating needs, while maintaining quality care.

Greater access, a capacity of caregivers, and operational efficiency shouldn’t be tradeoffs. The real hurdle is not technology, but changing how we operate.

Photo: claudenakagawa, Getty Images

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