Case study: shaping the health of populations with predictive biomarkers
Last month, we shared with you how Dr. Philip Chen, Chief Informatics Officer at Sonic Healthcare, maximized his lab’s services to strengthen their contract negotiations. His lab used predictive biomarkers to return to care patients with diabetes and chronic kidney disease (CKD) who had been lost to follow-up.
Now, we’ll take a more in-depth look at Dr. Chen’s initiative and how his lab is using it to influence the entire health system and improve outcomes in high-risk populations.
It’s a model that can help us all shape the very future of population health.
Contributing Lab Leaders
- There is hidden potential in the predictive biomarkers you see everyday
- One US lab is using predictive biomarkers to reduce costs and mitigate risk in specific patient populations
- Focusing on patients lost to follow-up can help your lab become an influencer for the entire health system
Philip Chen, MD, PhD
Chief Healthcare Informatics Officer
Avoid costly care
The goal of Dr. Chen’s initiative is to keep high-risk patients out of the hospital. This helps reduce Medicare costs for hospital stays. It can also reduce the human cost that disease-related complications exact on lost-to-care patients—those who eventually end up in hospital emergency departments.
On the high costs of Hospitalization
Begin with biomarkers
In Dr. Chen’s lab, efforts to keep patients out of the hospital begin with looking at predictive biomarkers in 2 high-risk populations:
A1c in patients with diabetes
EGFR (estimated glomerular filtration rates) in patients with CKD
Using ADA (American Diabetes Association) and NKF (National Kidney Foundation) guidelines, Dr. Chen’s team identifies patients at-risk. They then narrow down these data sets to patients who have not seen their physician in a year or more. In other words, those who are “lost to follow-up.”
Go directly to the lab
If Dr. Chen’s lab is working with an ACO (accountable care organization), they may simply hand these data over in a report to the ACO’s care coordination team, who will take responsibility for finding patients and returning them to care.
But in other instances, Dr. Chen’s lab contacts patients themselves, on behalf of physicians. Such direct interaction allows the lab to schedule patients for necessary lab tests before their return visit to the physician. This reduces costs by consolidating the typical 2 physician visits (1 before the lab tests, 1 after) into a single visit.
On visit consolidation
The consolidation conundrum
But there are 2 sides to the visit consolidation coin. This is because we’re straddling the fee-for-service and value-based reimbursement worlds.
Click the consolidation flashcard below to see the 2 sides of the issue—and how to use it to affect change.
Sharing informatics on patients lost to follow-up can have a profound effect on ACOs, payers, and physician practices—and on their relationships with labs.
In Dr. Chen’s case, he shared with the CEO of an ACO the percentage of their patients with diabetes who were lost to follow-up. The number was eye opening to the CEO and opened doors of opportunity for Dr. Chen’s lab.
Sometimes, the informatics Dr. Chen’s lab share can be overwhelming to practices. For example, his lab once issued a 70-page report identifying 1,200 patients with diabetes from a single practice that were lost to follow-up.
The response from the practice was understandable: whom do we bring back in first? Some type of risk stratification is required to create a manageable list of patients with the greatest needs.
But in a case like this, risk stratification may be beyond the purview of what the lab can offer. Fortunately, there are resources out there that can help. Dr. Chen cites the Johns Hopkins Adjusted Clinical Groups® (ACG®) system as 1 example.
He sees an essential role for laboratorians in bringing the issue of risk stratification up and working collaboratively to solve it.
On risk stratification
Make your mark with biomarkers
Think about the predictive biomarkers you see every day in your lab. Use your data to identify patients lost to follow-up in just one disease state. Provide the informatics to a practice or payer and watch how they respond. As Dr. Chen says, these are the types of initiatives we must promote and proceed with to control costs and ensure better outcomes for patients under population health.This is a paragraph of placeholder text. It is only here to help show the layout of the page and how the text will flow. Replace this placeholder text with your own meaningful content.
An introduction to this software that measures the morbidity burden in specific patient populations, using claims information and data from electronic medical records. A useful risk stratification tool.
This web-based system from the Joslin Diabetes Center in Boston, Massachusetts can be used for risk stratification, analysis of gaps in care, and much more.
This study demonstrated the effect of SMS (short message service), or text messaging, reminders on reducing loss to follow up rates in patients with type 2 diabetes.
This article highlights predictive biomarkers that can be used to establish action plans to improve patient outcomes.