Care Management Analytics for ACOs to Reduce Costs by Millions of Dollars

Care Management Analytics tools are in high demand by large Accountable Care Organizations (ACOs) that coordinate dozens of hospitals, hundreds of patient care sites, and hundreds of thousands of insured lives across Medicare, ACA exchange, Medicaid, commercial, and direct-to-employer lines of business. They report significant reductions in annual spending for Medicare patients, in hospital readmissions and lengths of stay (LOS), as well as improvements in care coordination for super-utilizers, who usually consume a disproportionate share of healthcare resources.

Strategies that improve care coordination allow organizations to measure shared savings, assess financial impact, calculate true ROI, save millions, and confirm projected annual savings of tens of millions over several years.

However, organizations often lack the appropriate analytics tools to evaluate the effectiveness of a care management program, especially when it comes to differences in patient populations served, the special needs of unique patient groups, and identifying where the deployment of care management resources can have the greatest impact.

Using data analytics and some key organizational measures helps solve such problems and achieve business goals faster.

Challenges with Care Management for ACOs

Organizations frequently encounter a range of different obstacles when assessing the care management program’s overall effectiveness. Healthcare systems must address the following:

Coordination Challenges

• Leadership might struggle to identify where allocating care management resources would be most effective
• Different outcomes of interest (clinical, patient-reported, operational, economic, population health)
• Variations in patient populations requiring care
• Variety of program offerings

Resource Allocation and Program Design

• Lack of a care management strategy that would enable a more efficient response to the needs of unique patient groups
• Lack of programs to improve treatment outcomes for unique patient populations
• Certain groups of patients may needlessly receive a disparate number of resources in the emergency department (ED) for their treatment when they could have received better care outside the ED
• Inefficiencies in addressing super-utilizers, such as homeless individuals with health inequities (visit emergency departments at rates far greater than those who have a place to live)

Communicating Value

• The care management team can have trouble clearly explaining the complete value of its program offerings

Methodological Challenges

• Propensity score matching requires choosing the right variables because it involves a trade-off between precision, generalizability, sample size, balance, and external validity
• Troubles forming control groups

Data Issues

• Lack of the required data

Features of Care Management Analytics Software

To drive informed decisions, leaders of healthcare systems leverage high-quality data,  analytics systems like Care Management Analytics software, and methods such as propensity score matching for evaluating the effectiveness of care management programs and assessing performance. Here are the tasks the software helps deal with:

Data Preparation for Analysis

• Setting enhancement objectives for particular agreements, geographical areas, and patient groups, informing the creation of service frameworks needed for progress
• Preparing comprehensive, high-quality data that includes measures of utilization and quality, and patient characteristic data, to support the evaluation of care management program impact
• Selecting the right variables to get results that clearly show areas of success and failure
• Determining appropriate control groups for comparative analysis via propensity score matching
• Performing propensity score matching analysis to compare and assess the difference in outcomes and costs between patients getting care management interventions and similar patients in a control group who do not get such interventions

Insight Generation and Visualization

• Visualizing the care management analysis results to easily discover new insights
• Recognizing patients with numerous factors affecting their well-being and health and enlisting them in a care management program
• Identifying and analyzing differences in outcomes and costs across multiple payers for hundreds of thousands of lives

Program Outcome Assessment

• Assessing how effective patient outreach activities and different care management programs are
• Determining programs with the most potential success and programs that may need to be corrected or considered for constant improvement
• Evaluating the financial outcomes of care management programs.

Organizational Measures to Implement Care Management Analytics Tools

It is recommended that healthcare systems adhere to the following initiatives:

Building a Multidisciplinary Team and Using Analytics for High Utilizer Care Programs

• Organizations should involve data engineers, data scientists, data analysts, industry professionals, and financial experts, all with a deep understanding of care management programs and patient needs and characteristics, to work together on the project and choose the relevant variables.
• Health systems can use insights from analytics tools to design cutting-edge care management programs for high utilizers.

Analytics-Driven Initial and Post-Discharge Engagement

• Community health workers (CHWs) should use data analytics to identify high-need patients and arrange the first meeting when they are in the emergency department or hospitalized
• CHWs should base their intensive navigation strategies on analytics-driven insights to support super-utilizers after hospital discharge, focusing on harm reduction

Linking Patients to Appropriate Services

• CHWs should use analytics tools to prioritize high utilizers and tailor their in-person meetings (1+ times per week) to address patient goals and needs
• CHWs can use analytics to link patients to the most appropriate medical, mental health, or community services, reducing unnecessary repeat services
• CHWs should rely on analytics to identify and schedule the most impactful mental health, medical, and benefit appointments for high utilizers in special programs

Benefits from Using Care Management Analytics Tools

By coordinating efforts and using data analytics software, healthcare systems can assess the financial impact of their care programs, measure the actual return on investment, and determine shared savings. The results reported include:

Financial Savings and Cost Reductions

• Tens of millions of dollars in anticipated yearly savings over several years
• Millions of dollars in savings as a result of the decreased resource utilization
• Significant reduction in annual costs for Medicare patients under the care of the proactive care team
• Reduction by hundreds of thousands of dollars over approximately one year in payers’ costs for patients enrolled in innovative care management programs.

Utilization and Service Optimization

• Significant reduction in utilization for commercial members that get hospital transition outreach
• Significant relative reduction in readmission rate
• Considerable relative reduction in the time patients spent hospitalized and length of stay  
• Notable decrease in ambulance transports, which frees ambulances for medical emergencies
• Serious lowering in emergency department visits, thereby increasing the capacity of departments for patients who require emergency care

Improved Patient Outcomes

• A notable improvement in the quality of life for targeted patient populations who participated in special program offerings, such as a multiple times increase in the number of cases when a homeless received housing after discharge from the program

How Belitsoft Can Help

Belitsoft is a full-cycle software development and analytics consulting company that specializes in healthcare software development. We help top healthcare data analytics companies build robust data analytics platforms.

For integrated data platforms developed to collect, store, process, and analyze large volumes of data from various sources (Electronic Medical Records, clinic management systems, laboratory systems, financial systems, etc.), we:

• Automate data processing workflows (cleansing, standardization, and normalization).
• Configure scalable data warehouses.
• Set up and implement analytical tools for creating dashboards, reports, and data visualizations.
• Ensure a high level of data security and compliance with healthcare regulations such as HIPAA.
• Integrate machine learning and AI into analytics.

We also help build specialized analytical applications like Care Management Analytics tool for:

• Rapid and efficient entering of patient data (including social determinants of health), patient flow data, as well as payer data for continuous program evaluation and data-informed decisions
• Analyzing data that describes patient demographics (age, gender), clinical condition (diagnoses), disease cohort, as well as utilization data
• Evaluating patients based on utilization criteria and screening them for social determinants of health that influence health consequences (housing status, job status, financial hardship, violence, food instability, and incarceration)
• Visualizing various care management use cases and performance evaluation
• Estimating the expected cost savings for every effective patient outreach by the reason for the outreach and outreach type
• Demonstrating how transition support from a care staff for a particular period of time after discharge is related to reduced costs for patients with a certain disease and insurance plan.

If you're looking for expertise in data integration, data infrastructure, data platforms, HL7 interfaces, workflow engineering, cloud development (AWS, Azure, Google Cloud), hybrid or on-premises environments, as well as data analytics, we are ready to serve your needs. Contact us today to discuss your project requirements.