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The Future of US Healthcare – Learnings from HIMSS 2016

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HIMSS 2016 has come and gone, and, boy, did it leave us with a lot to take in.

US Healthcare, for good intention and cause, brought itself into a state wherein every healthcare practice and every EMR vendor made their own standards. If it were good intentions, how did this siloed and differential standards state come into existence. It was because of lack of minimum or basic standards/guidelines. Now, with advent of HL7, CDA, FHIR, and the HIway projects, the industry has got certain guidelines to follow.

Are we out of control? No. Healthcare providing organizations,  health networks, and Vendors are trying to bring them closer to these standards. The only question remains, can we make it faster to give way to analytics on top of EMR data, driving population health management better, identifying best risk models, using other data signatures like genomics, local demographics, and social media with clinical data to make predictive and decision support models better? Given below are key takeaways from HIMSS 2016:

Speed of Integration – Health Data Integration still remains a challenge. It’s solved, it’s doable. Although speed is the key now as organizations are trying to generate newer insights and analytics drawn from EMR data.

Interoperability, rather Interoperability first – Interoperability is the new theme of this later half of the decade. But if you go deep and pull out the true story, even intraoperability remains a challenge as deltas are not even exchanged within departments of same network. Medical Device companies are working closely with EMR vendors to bridge this gap in almost real-time as machines and EMRs start talking to each other.

Healthcare and IoT – Efforts had already been made towards collecting data from medical or physical devices installed in medical facilities to perform real-time actions for better care, for example: alerts going to nurse if a patient has left the bed. The industry is moving towards a trend of using data collected for these devices to enable their decision support systems.

Population Health Management – The broader idea of population health management remains the same which is: Identifying Population, Stratifying Population, Care Coordination, and Patient Engagement. The way every piece is done and with what speed is still being experimented in the industry in several ways. Faster speed of health data integration is the key to identify exhaustive population at speed. CMS/HHS recommended risk models, proprietary risk groupers, and home grown solutions which are used to evaluate risk and stratify population. Age, Gender, and Clinical Conditions were three primary features that were used to predict risk, while genomics (or an individual’s gene markers) is being introduced as the fourth feature to improve risk models.

Revenue Cycle Management – It is an easy problem to solve with two difficult components associated to it:
1) Closing the loop
2) Customizing to your workflows.

All vendors in the market in revenue cycle management are focusing on few areas of revenue cycle management while providers are looking for solutions which can help close the entire loop starting from diagnosis to revenue being collected in the same stack. Secondly, every provider organization has a different way of working with differential payment contracts and is looking for a solution which is flexible enough to customize to their workflows, governance structure, and payment structure.

Kanav Hasija

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