COVID-19 has challenged healthcare systems globally and collectively required all of us – healthcare professionals, patients and the communities we serve – to think and expect differently. Our responses to the pandemic in the form of innovations, changes to processes or even our roles have shattered many pre-conceived biases of what is doable or not. As the new reality takes shape, we need to move beyond being just passive observers, progress past the short-term fixes and optimize the entire healthcare system from public health institutions through to private care providers. We saw that innovations are becoming more common and necessary in this future state of digital health. When embarking on innovations, we need to take into consideration patient safety and quality as critical metrics. We do not need more digital technology; we need digital transformation that is defined to meet the challenges of our new collective reality.
Furthermore, the COVID-19 pandemic provided a compressed timeline of what typically transpires over a period with regard to evolving medical research and the challenges of translating research knowledge to patient care. It takes up to 17 years for established treatment knowledge to be consistent and reliably applied in patient care. During time of crisis, this unacceptable lag becomes completely non-negotiable. We identified that resource constraints in terms of staff shortages or siloed staff roles impair how rapidly a healthcare organization can react and redeploy clinicians to changing priorities and needs. Moving forward, new knowledge acquisition and obtaining patient care skills will need to be closer to just-in-time learning models throughout the professional careers for many clinicians.
The new reality of life with COVID-19 after the initial crisis of the pandemic will require sustainable models of care, one that is driven by knowledge. Enabling and supporting such dynamic care delivery models will require a system level design thinking that simultaneously accounts for three key dimensions – data flow, clinical workflow and decision flow to ensure that the care process is a knowledge-driven care process. Essentially, what clinicians need, to both perform their tasks and make the critical clinical decisions regarding care align with the care processes; and what patients can expect is a level of care and consistency that aligns with the latest body of evidence-based knowledge and best practice standards, delivered via an optimized consumer-centered experience that is efficient, and cost-effective.
The same concept was also illustrated by Dr NT Cheung in the first webinar of the Future of Healthcare series.
“We started with three pillars – one, which is developing an electronic patient record in a very standardized, structured, comprehensive way that’s always accessible by all caregivers to a patient. Two, supporting the process of care such that it enhances the workflow. And lastly, improving quality and safety.” Dr. N.T. Cheung, CMIO, Hong Kong Hospital Authority Episode 1: Creating a sustainable model of healthcare
Knowledge- Drive Care Process Framework
Knowledge-driven care consists of the flow of knowledge to support clinical care activities (i.e. workflow), clinical decision making across time and location (i.e. the flow of decisions), and access to the required data necessary to support care and clinical decision making (i.e. data flow).
Data Flow Key to knowledge is the availability of quality, machine interpretable data for easy access by clinicians and patients and leveraged for decision support.
Decision Flow
Along the workflow for a care process, many clinical decisions are made by clinicians. These decisions take place in the context of the clinical work and activities, requiring key information needed to make the best decisions regarding care and treatment.
Clinical Workflow
Clinical care is a series of complex, multi-disciplinary process that often cross clinicians and organizations, geography and time through the care continuum. Each step in the workflow has different contextual needs for data to support work and decision making
Knowledge flow
The optimization and alignment of all three dimensions ensures that for a given patient care journey, knowledge is in fact flowing and supporting the entire care process and driving toward optimal outcomes. Knowledge flow is achieved when clinical care activities are optimized (i.e. clinical workflow) based on accurate and coordinated clinical decision making (i.e. decision flow), and taps the right data necessary (i.e. data flow) to support the entire care continuum and clinical decision making process.
Chuang is the Chief Medical Officer, EMEA, Latin America, APAC, Elsevier
