Reducing Hospital Readmissions with Healthcare Analytics

An important area in improving patient outcomes and making hospitals more efficient revolves around the challenge of patient readmissions.

It’s a problem that impacts patient health, hospital finances and taxpayer pocketbooks.

How big a problem? Nearly 20% of elderly patients are readmitted to hospitals within 30 days after being discharged, according to a study published in the New England Journal of Medicine. About a third of all discharged patients return to the hospital within 90 days.

Healthcare analytics is providing a possible solution. Using historical data and trends, hospitals can leverage predictive analytics to determine which patients are most likely to need readmission.

This allows medical professionals to take steps in patient treatment and education that can decrease the likelihood of a patient being readmitted.

Problems Involving Readmissions

The impact of readmissions has broad implications for patients, hospitals and taxpayers.

First and foremost are patient outcomes. It seems logical to assume that those who need readmission to a hospital have not properly addressed the issue that hospitalized them in the first place. But perhaps in many cases, it’s that they have not received the level of medical care, education about their condition and post-release monitoring needed to prevent readmission.

For hospitals, the cost is significant. During the Obama Administration, penalties were put into place for hospitals who have too many Medicare patients readmitted in 30 days or less. These include patients with certain lung ailments and those who underwent hip and knee surgery or had a heart attack.

The penalties can reach as high as 3% of the total annual Medicare payments to a hospital.

Those penalties are continuing under the administration of President Donald Trump. Medicare is penalizing 2,753 hospitals in 2017 for a total of about $564 million.

Readmissions regularly hit taxpayers, as well. In 2011 alone, 3.3 million patients returned to the hospital within one month of release, according to a study from the Agency For Healthcare Research and Quality. Those trips back the hospital cost a total of about $41 billion for the taxpayer funded Medicare program.

Analytics Used To Lower Readmissions

Facing these penalties, many healthcare organizations have turned to analytics to improve patient outcomes and reduce readmissions.

Using patient data compared against historical numbers on readmissions, analysts can flag patients who have indicators that show they are likely candidates for readmission.

The factors are numerous. They include the number of recent hospital visits, the length of the original stay in the hospital, the severity of the original issue that landed the patient in the hospital in the first place, past drug use and socio-economic factors.

Analytics also provides a way to find solutions and testing that work. They can range from different medications to more frequent communication. Some examples include the following:

Satchel Health

Nashville-based Satchel Health has developed a telemedicine care management platform that allows patients to consult with clinicians via video without having to make a trip to a clinic. Founder Ryan Macy started the company after his grandmother died from cancer while in a skilled nursing facility.

He saw firsthand how some patients who need consultations and rehabilitation after a hospital visit find it difficult to make appointments. It’s also harder for medical professionals to spend time at such clinics with patients. The Satchel Health software platform increases the ability for clinicians and patients to meet and discuss positive steps in treatment.

UnityPoint Health

Des Moines, Iowa-based UnityPoint Health operates hospitals and clinics across Iowa and Illinois. They used data analytics to flag patients at high risk for readmission. They then took it a step further, looking at the chance of readmission every day in the first 30 days after discharge, based on historical information from similar patients.

This allowed medical professionals to focus on consulting with patients in the “heat zone” during which they most likely would have situations that required readmission. They also flagged patients who had a high risk of not showing up for follow up appointments and took steps to ensure they saw a clinician when appointments were scheduled.

The effort has led to a 40% drop in patient readmissions.

Allina Health

Allina Health runs 13 hospitals, 90 clinics and 16 pharmacies in Minnesota and Wisconsin. Data analysis from their operations indicated that 40% of patient readmissions actually occur in the first week after discharge. Allina Health decided to focus on this critical first week.

They first designed a standardized set of criteria across all their operations to determine if a patient is a high-risk for readmission. They then designed a standardized approach to care for these patients. This included a meeting before discharge between the patient, hospital transition team, the patient’s family and in-home health providers. They also educated patients on beneficial steps they could take to maintain good health and pitfalls to avoid.

The company saw a 10.3% reduction in patient readmissions and saved $3.7 million.

Those represent a few examples of data and technology being put to use to improve patient outcomes and reduce readmissions. Overall, the costs upfront are more for a hospital, according to the Harvard Business Review. But the long-term benefits justify the investment.

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