Population Health Analytics to Keep High-Risk Patients Healthy and Costs Low in the Long Run

In the U.S. healthcare system, individuals with chronic and mental health disorders represent 90% of the $4.5 trillion in healthcare spending annually. Every one in four U.S. patients suffer from two or more chronic conditions.

Large healthcare networks that operate tens of community health centers, several hospitals, and a few academic medical centers, employ tens of thousands of staff and serve millions of patients annually to develop integrated care management programs. Those programs aim to determine high-risk patients, mostly Medicare beneficiaries, to improve their health outcomes, avoid complications and hospitalizations, and consequently reduce costs. Accountable Care Organizations (ACOs) also benefit from care management programs, as they are rewarded for achieving better health outcomes while managing costs effectively. Healthcare analytics like population health analytics complement care management programs with tools that make data gathering and analysis easier and more efficient. 

In this post, the Belitsoft healthcare software development company shares the ideas about the challenges of maintaining care management programs, helpful functionality of the analytics applications, and their benefits.

Challenges in Providing Integrated Care for Complex Patients

High-risk patients demand costly care that consists of medical, social, and economic conditions they live in. In combination with chronic diseases, those conditions make the routine of patients, their families, and care teams burdensome. Here is a list of difficulties healthcare networks have to deal with:

• The costs of the patients with multiple chronic conditions significantly exceed those of patients with only one condition as they demand more medical assistance.
• Adapting care management programs to the demands of complex patients and their social and economic peculiarities is necessary.
• Complex patients might involve such severe health conditions as cancer, heart failure, diabetes, asthma, and cognitive disabilities in one patient.
• Complex patients demand constant care both inside and outside of hospital walls, as they may forget to take drugs, and have difficulties breathing and communicating.

Features of Population Health Analytics

Healthcare networks implement data analytics tools in their workflows to define high-risk patients, enhance their health conditions, and cut costs. Such an approach allows for expanding care management programs and taking care of people suffering from multiple chronic diseases and aggravating social and economic factors. Here are the features that analytics software can demonstrate:

• Integrate data from various resources to include operational, billing, and clinical information into the process of identifying rising-risk patients.
• Embed risk-predicting modeling into the software to analyze claims data and forecast at-risk patients for the next year.
• Focus on a small number of ultra-high-risk patients who struggle with social or economic issues, behavioral difficulties, and medical conditions.
• Assess the efficiency of the care management programs and generate insights regarding both day-to-day operational and strategic decisions.
• Real-time updates of the electronic medical records (EMRs) about enrolling a patient in a care management program to inform specialists in emergency departments (EDs) and inpatient settings.
• Automated paging to notify care managers about their patients visiting EDs.

Benefits of Analytics for Care Management

Healthcare networks that have already applied population health analytics in the care management of high-risk and rising-risk patients, report the following improvements:

• Hundreds of dollars saved per patient enrolled in a care management program per a month period on the basis of claims data.
• Financial success of at-risk contracts within accountable care organizations (ACOs).
• Scaling the program to tens of thousands of patients in several years.
• More cost savings related to longer program membership.
• Decreased relative risk of ED arrivals, non-emergent ED arrivals, and hospitalizations during a target period with increasing time of program participation. 
• Reduced levels of mortality among the patients enrolled in care management programs.
• Extending and customizing the program to other healthcare organizations with an aim to improve patient outcomes.

Organisational Measures for Smooth Analytics Implementation

Population health analytics helps healthcare organizations identify patients with multiple medical, economic, social, and behavioral challenges and enroll them in integrated care management programs. However, the implementation of powerful software is not the entire process. A well-coordinated work of healthcare experts is required. Therefore, healthcare networks assign population health teams. Those teams examine conditions and rebuild clinical care to make it focus on the whole patient. Members of the teams cooperate with frontline clinicians to develop care strategies and compose programs, and tools. They design, apply, and manage a systemwide care strategy based on value for all population groups.

Population groups selected for enrollment into care management programs may include the following patients:

• Pediatric patients, adult patients, and elderly individuals.  
• Patients with multiple health conditions.  
• Patients with mental health issues, behavioral health problems, or substance use challenges that exacerbate existing medical conditions.  
• Patients who lack the socioeconomic resources necessary to manage their illnesses effectively.  
• Patients at risk of becoming high utilizers of healthcare services.

The lists of patients who can benefit from care management programs are formed not only with the help of analytics tools. Primary care physicians review those lists and can suggest other potential beneficiaries. Patients may suffer from several serious chronic conditions, such as heart dysfunctions. They might be homeless, homebound, abused, struggle with depression or substance use disorders. Any of such conditions might become a prevalent health driver. Care management programs sometimes have to deal with all the drivers simultaneously, if patients are medically complex.

Program developers apply a triad care team model, i.e., they match high-risk patients with care coordinator leads. The lead might be a nurse, community health worker, or social worker depending on the needs of patients. The lead works in close cooperation with the patient and their family. They develop a specified strategy of care that includes regular contact with the patient, arranging necessary tests, logistics, social help, doctor visits, and other activities required.

Care management programs also include activities of care managers who monitor a patient’s health condition and track vitals, medications, and tests to make sure the care plan allows for a stable condition and keeps a patient out of the hospital. Care managers work closely with primary care providers to guarantee they know patients’ statuses and will immediately report any issues.

Besides care managers and leads, there might be cooperation with community-based healthcare organizations. They provide support outside the hospital walls. For instance, they can arrange patients’ time at home, in public places, or other required locations. They also help with transportation to primary care offices and interpret the information for patients if needed.

How Does a Healthcare Software Development Company Help?

Healthcare software development companies like Belitsoft offer their expertise to data analytics companies in building and customizing data analytics applications and platforms. Such tools enable users to analyze their current performance indicators, identify areas for improvement, and coordinate workflows.

Integrated data platforms perform such functionality as collecting, storing, processing, and analyzing large data volumes from sources like EMRs, clinic management systems, laboratory systems, billing systems, etc. Those platforms can do the following:

• Automate workflows, such as cleansing, standardization, and normalization.
• Set up scalable data warehouses.
• Implement analytical tools for designing dashboards, reports, and data visualizations.
• Configure data security algorithms and comply with healthcare regulations such as HIPAA.
• Integrate machine learning and AI into analytics.

Healthcare software development companies are also ready to create specialized analytical applications like Population Health Analytics for: 

• Real-time data updates.
• Embedded algorithms for risk stratification.
• Predictive analytics to assess rising-risk patients.
• Automated visualizations and reporting.

The Belitsoft healthcare software development company is also an expert in data analytics, data infrastructure, data platforms, HL7 interfaces, workflow engineering, and development within the cloud (AWS, Azure, Google Cloud), hybrid, or on-premises environments.