CTI Healthcare blog series: A Prescription for Improving Patient and Institutional Financial Wellbeing
Healthcare’s Data Revolution: It’s Complicated – Part 2 of 5.
In my last blog post The Accelerating Importance of Data Analytics in Healthcare, I discussed how and why business intelligence is fast becoming a critical strategic driver for health care. In this blog I’ll explain why the Health care Data Revolution is complicated.
Over the last 18 months, we have been implementing data analytics solutions that impact the core of how health care services work.
Of course, It’s just another example of the inexorable drive that organizations have to improve systematically, and its old news that “data” holds the keys to how.
I’ve applied data analytics in many different industries and functional departments, and each time it’s so rewarding to see the potential for impact. All data comes from somewhere, but unfortunately, for many health care providers, it doesn’t always come curated with impeccable data governance practices. The challenge the industry faces is to establish processes and best practices to ensure that data is useful for downstream analytics. Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.
Here’s how I’ve come to understand the healthcare system landscape:
The primary constituents are the Payers, the Providers, and the Patient. Payers are the health insurers, Providers are the hospitals or any medical office, and Patients are you and me. The Provider performs the medical service on the Patient, which the Payer helps with the payment. The Payers are involved before and after a medical procedure is completed – they first approve the procedure and later process a claim from the Provider to reimburse a portion of the line-item costs. The Patient often also has responsibility for some of the costs, and the Provider will seek some reimbursement from them too.
The fee-for-service system is inherently lopsided in that the money is exchanged per visit or procedure being performed on a patient. This creates the unfortunate effect that it is financially advantageous for Providers if Patients are sicker; and less beneficial to prevent sickness in the first place. Not, of course, that providers want patients to be sicker! Preventative healthcare has an uphill battle, which can only be solved or financed by structural change and innovation in the reward systems. While that topic is way too big for me, it is already being energized by the power of data and data analytics, and by the ballooning healthcare costs under today’s lopsided model.
The health care industry is however well on its way to transitioning to paying for value over volume. A value-based reimbursement model is a data-driven approach based on patient outcome. Greater data sharing and interoperability should cut costs and improve care quality, both goals of a value-based model, by enabling providers to track their patients throughout the care continuum, giving them the opportunity to more effectively influence care. It can also ensure the information necessary for care delivery and payment is easily accessible to those who need it.
Poor quality EHR data, complex workflows, and incomplete data capture can all contribute to quality issues that will plague data throughout its lifecycle. Without effective, open data sharing, providers cannot keep patients healthy. Without data to track patient progress or understand quality, insurance companies cannot tie payment to outcomes.
CMS Administrator Seema Verma emphasized at this year’s HIMSS conference in Orlando. “Without effective, open data sharing, providers cannot keep patients healthy. Without data to track patient progress or understand quality, insurance companies cannot tie payment to outcomes.”
In my next blog, I’ll discuss the value of an integrated governed healthcare data ecosystem.
Read part 1
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