Can Analytics Save Healthcare?
By Christopher Malter, CEO of Avalon.AI
The U.S. Healthcare industry, prior to the pandemic, was somewhat focused on technologies that move towards optimal outcomes, both clinical and cost. That has changed since the onset of coronavirus. No area of healthcare was untouched: from massive data companies to device technology, from telemedicine to the integration of predictive analytic software. These trends have been accelerated with the urgency of the pandemic and the power of technology. Safe vaccines which once took years to develop, manufacture, and distribute now take months. Doctors’ office visits are now done via telemed. Wearable devices deliver real time data for individual patients and across patient populations.
The integration of advanced interpretive analytics, based on Artificial Intelligence and dashboard accessible real time data is another development that will impact the entire healthcare chain.
Hospital system C-suite executives, payers, healthcare systems, lines of service, and doctors are increasingly relying on and driven by predictive data. These types of data analyses have already been delivered for years in industries such as financial services and are now migrating to healthcare. The ability to monitor and measure revenues and cost effectiveness throughout the chain will largely determine how investors will value healthcare entities. The reverse also applies: the inability to deliver such analyses will leave investors with no method of meaningful valuation.
Apart from the investment equation, such capture, analysis, and interpretation of data directly affects the efficiency and productivity of healthcare delivery to patients – including, for example, block purchases of pharmaceuticals to measuring prescribing patters across patient demographics and physicians, by procedure. These can then be aggregated for composite analyses. Data points are interoperative so reporting can move in whatever direction is most effective for users.
The net effect is that each professional participant in the healthcare system now has a comprehensive and singular point of extracting data, and from that, identifying inefficiencies and then having a one-stop decision tool for increased productivity: at any point in the system, and across all physicians and procedures – in areas such as cost analysis, purchasing, and patterns of care.
Of even greater significance is the recent move of firms to move past predictive analytics – important though that is – to give a sense of optimal outcomes predictability. Here, the analytics moves beyond “what am I doing?” to “what should I be doing?”
The vendors who deliver these capabilities to hospital systems and their participants are increasingly on the radar screen of investors, as they provide "must have capabilities” to their hospital system partners. This market is relatively "blue ocean” as healthcare is rapidly ramping up to more sophisticated SaaS Cloud platforms. As industry standards emerge, first mover providers will secure higher multiples as a function of product sophistication and the capture of market share.
The global pandemic has demonstrated multiple points of failure in healthcare. The now available analytics that will identify these gaps in advance will mitigate any effects in the future. The identification of the failures have come at a high institutional and human cost. As with most hindsight views, the failure has accelerated the response to making sure systems are in place so that in the future healthcare is never again caught short.
Inefficiencies invite technology solutions. Firms that address these inefficiencies will likely prove to be attractive to the investor/investment communities.
Christopher Malter is the CEO of Avalon.ai, an AI platform dedicated to developing real time enterprise solutions connecting customers with end users through smart technology.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.