By Dr. Ian Chuang and Dr. Richard Loomis, MedCity News | August 25, 2019
Dr. Ian Chuang will be a panelist at the upcoming 2019 DHIT Summit in Durham on November 13th. In addition to senior roles at McKesson and Cerner, Dr. Chuang’s previous positions include Chief Analytics Officer at Medecision, SVP of Healthcare Informatics at Netsmart, and VP of Medical Informatics at Cigna.
AI and machine learning can transform data from health records — “real world data” — into “real world evidence” that will bolster the medical community’s collective knowledge exponentially. Yet, clinical decision support platforms are the only way all this added practice-based knowledge can actually be used to improve overall care.
Provider Insights, Patient Empowerment
A common issue, typically for hospital leaders in countries of both developing and developed healthcare systems, is the difficulty in narrowing the “inequality gap.” This refers to the gap in the standards and quality of healthcare provided across institutions – even those within the same network or geographical boundary.
Further, as providers gradually shift from traditional models of care delivery towards telehealth and telemedicine, where alignment and care coordination are based on a common set of care principles or pathways, bridging the inequality gap is even more important.
To truly improve the quality of healthcare, we must leverage the benefits of digital transformation and integrate insights gleaned from that with clinical experience. By implementing a fully integrated knowledge learning and sharing platform that overcomes the limitation of time, space and human capacity, advanced technologies like artificial intelligence (AI) and machine learning can provide HCPs with personalized insights to better inform clinical decision-making.
HCPs can draw from the underlying data, evaluate each unique patient’s circumstances and make personalized treatment options at the point of care. Patients are empowered with this knowledge, and this helps to create a truly personalized care plan through increased patient empowerment and shared decision making.
Digitization of Healthcare: Impacts on Clinical Practice & Decision Making
The rapid increase in medical knowledge and digitization of healthcare data are changing the way knowledge is used and applied in clinical decision making.
Currently, clinical research data is the foundation of evidence-based medicine, yet it takes roughly 17 years for only 14 percent of new scientific discoveries to find their way into daily practice. Knowledge will remain distant and disjointed from clinical care if it is confined to the digital library.
Hence, as the digitization of health records produces more data relating to patient health status and the delivery of care, AI and machine learning can transform this ‘real world data’ into ‘real world evidence’ that will bolster the medical community’s collective knowledge exponentially. Yet, all this added practice-based knowledge will still not improve overall care unless it can be fed back into daily practice via an operationalized platform like Clinical Decision Support (CDS).
CDS is broadly defined as a set of process, knowledge content and digital functionalities that are designed to integrate with the HCPs workflow at the point-of-care to support and guide clinicians along a common set of care references. CDS helps ensure the delivery of the most current evidence-based care in line with evidence-based guidelines and best clinical practices. This unifies the care experience that can reduce medical errors and can help to deliver higher quality care.
HCPS can use a combination “pull” and “push” CDS solutions to achieve this. ‘Pull’ solutions allow users to seek and find current and credible evidence-based information to guide care. ‘Push’ solutions automatically deliver, or push, knowledge and information specifically needed to the user, ideally in an executable form.
Knowledge, by many HCPs, is acknowledged as the backbone to improving practice, but practically speaking it can be challenging – especially when making life-and-death decisions in the operating room, at the bedside, or under time pressure. With the vast memory and consumption capabilities that machine learning has to offer, HCPs are better able to scale the entire universe of medical knowledge. The clinical imperative is no longer who knows the most, but rather who can get to the most current and relevant evidence-based knowledge at the point of clinical action.
In a time and resource-constrained world, application of real-world evidence and CDS tools reduce cost and inefficiencies from the point that a decision is made to admit to the decision to treat. CDS also provides the use of patient-specific healthcare data to support the optimization of care decisions, improving precision and personalization of care.
Reducing unintended variations improves the quality of data we feed into the system, and through machine learning and AI, we can move closer to prescriptive and even predictive capabilities to deliver precision care at the patient level.
Digital Transformation in the Future State
While the democratization of healthcare still has a long journey ahead, technology and the digitization of healthcare continue to lead the path. HCPs must be willing to pledge a commitment to digital transformation in order to improve evidence-based care. They need to invest time and resources to adopt a knowledge-first mindset, and a knowledge-powered system to eliminate unwarranted, unnecessary variation, and standardize the quality of care.
Only then will patients experience better outcomes, while costs will reduce and we will end up with a healthier, more informed and empowered society overall.