Healthcare agencies entering risk-based contracts mostly do not adequately think how difficult it will be to stratify the risk of sufferers who will be treated under the contracts, and as an outcome, they do not get the results that they envisioned.
The reason is very simple, Chilmark Research asserts in a new report. Only 10% of results are driven by medical care, 20% of results are driven by genetics, and 70 are deployed on individual behavior and social context, claims Jody Ranck, an analyst at Chilmark and lead author of the report.
Moreover, behavioral and social data assist clinicians to observe the barriers that sufferers face, like not being capable to walk in the neighborhood each day due to high crime rates or the inability to pay for medications.
Risk stratification was established by healthcare payers to launch fairness into physician compensation based on sufferer severity, claims Ranck. Now, new models of risk stratification focus not merely on triaging high-risk sufferers but on what to do to keep them from utilizing excessive rates of medical services.
That is a huge change in approach, because physicians have been paid for triaging—incentives were such that doctors gave routine care and the onus was on the sufferers to follow their suggestions—if sufferers did not follow instructions, they just returned for more care, and physicians got an extra payment for that care encounter. That will not wash in a latest era of accountable care, where reimbursement will be deployed on quality, not the volume of services.
Accountable care needs access to real-time clinical data, patient-reported information and health assessments that can be fed into an analytics program, in accordance to Ranck. That is different from conventional data sources based on claims data and sufferer health risk assessment forms.
Although, getting behavioral and social information into EHRs is difficult, Ranck appreciates. But there are start-up companies emerging that could solve that issue over the next 5 years.
One of the newer vendors, Forecast Health, gathers 4,000 data elements on patients, like transportation options, finances, lifestyle factors and social media activity. Another vendor, Scio Health, utilizes claims, clinical, census and ZIP code information to understand risk well enough to intervene and reach out to sufferers.
Provider agencies can use these data to recognize sufferers that should be called by a nurse to observe why they are not adhering to their care plan; for instance, if the hurdle is transportation, a contributor might decide to give transportation services to pick up a sufferer for care. The data also can show which sufferers best respond to phone calls, texts or emails, as well as their literacy levels, and get personalized messages with scheduling options for appointments.
As these sufferers are being recognized and contacted, risk stratification can show if sufferers have a pattern of not showing up for appointments, resulting in subsequent hospitalizations, Ranck claims.
“You require a 360-degree view of high-risk sufferers and a strategy. What is the context of the sufferers, and how can we customize a care policy to keep them healthy? It is intelligence gathering and transferring that intelligence into an actionable intervention.”
For example, analytics can indicate that most falls happen in certain types of apartment buildings, and contributors can utilize this intelligence to find ways to decrease falls, which could lower hospitalizations.
With social factors conventionally being a hurdle to getting care, the job of making better the access to care has fallen on social services agencies, Ranck notes. Leaving the job completely to such agencies is not enough in an accountable care era. “It was always someone else’s job, and now it is the physician’s and hospital’s job to augment traditional social facilities.”
Consequently, contributors require to focus on the highest-risk sufferers under their risk-based contracts, then utilize the predictive analytics to find the next level of high-risk patients that could transition to become high utilizers of services, Ranck claims. “That is the holy grail of predictive analytics—seeking out who they are.”
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