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Trends Impacting the Care Manager

Executive Summary

Recent changes to federal mandates have provided increased transparency to provider and payer price data, empowering care managers to guide more informed cost-of-care decisions. Increased virtual and home-based care has provided care managers with additional opportunities to lead care coordination efforts, steering members toward more convenient, higher quality, and lower-cost of care options.

Care managers increasingly have improved options to leverage data and AI enhanced tools, such as AI enabled risk stratification and patient engagement to reduce administrative costs and improve member outcomes.

Care managers are also integrating more into local communities to better understand how resources can meet member needs and improve health outcomes in targeted populations. Care managers will need to work more closely with providers to gather and address social determinants of health (SDoH) needs via various screening tools, including screening patients for non-clinical risk factors during their primary care visits or inpatient admissions.

Payers have started to increase their investments in technology and partnerships with local community resources to enable care managers to meet members where they are and by means of communication that they are most comfortable with.


1. Price Transparency

At the beginning of 2022, the Transparency in Coverage rule enacted by the Centers for Medicare and Medicaid Services took effect. Payers and providers must now disclose detailed pricing information to the public around negotiated rates, out-of-network billed charges, and prescription drug prices. Later in 2024, care prices will become more accessible with a self-service tool and shoppable services list requirements.

The Transparency in Coverage and Hospital Price Transparency rules increase consumers’ ability to make informed economic decisions about the care they receive. The intent of the rules is that visible provider and payer pricing data will begin to level the playing field, raise competition, and create savings opportunities. Care managers and care management systems enabled by price data could well have an enhanced role moving forward.

When evaluating care management systems, payers should consider solutions that enable the incorporation of transactional data. With data as a resource, leading care management solutions will consume price data, transcribe insights per applicable members with AI, and then report pricing options to care managers, while they discuss with a member. An AI empowered price module will better enable care managers to steer members to lower-cost, higher-quality care.


2. Virtual and Home-based Care

The COVID-19 pandemic has accelerated home-based and virtual care to new heights. Payers actively steer patients through cost shares and utilization management (UM) controls from high-cost centers such as hospitals to high-quality low-cost options. Current legislation may provide health systems with an additional opportunity to participate in value-based programs through home health1,2. Care access options continue to increase and grow in complexity. This system is challenging for the average person to properly understand, let alone a person suffering from a medical hardship. Payers have an opportunity to seek care management solutions that enable their care managers to be the center of virtual and home-based care, providing a streamlined experience for members.

In most products, health plans are taking on risk, and therefore it makes sense to have a health plan clinician coordinate care with providers to optimize care delivery and ensure patients receive optimal care. Placing the care manager into the care coordination mix provides an opportunity to drive better care communication and improved outcomes. Once the PCP deems a referral is needed, the care manager steps in to provide patients with care options utilizing price transparency and quality information.

Care management solutions with home-based and telehealth coordination capabilities have the potential to move the cost of care needle significantly. A platform that provides an inventory of telehealth and home care resources with pricing data can open substantial opportunities. A care inventory list will empower care managers to fully inform and educate patients on available care choices. With price in mind, members will be able to choose care that makes the most sense for them, with home care and telehealth as cheaper and more convenient options.


3. Artificial Intelligence

As technology continues to develop, payers will be able to equip care managers with cutting-edge AI. Imagine a machine that takes records from all visits and sites, calibrates patient needs (resources, date of next preventive exam, etc.), and automatically displays the report when a care manager engages a patient.

Further AI development in the care management space will take risk stratification to the next level and allow care managers to engage more quickly and comprehensively with patients most in need.

In any health system, a small number of patients disproportionately account for a significant percentage of healthcare related costs. If these high-cost patients could be identified as early as possible and supported with robust preventative care, it is likely to improve patient health outcomes while maintaining an overall lower cost of care for a health plan by limiting future hospital admissions and complex procedures. Risk stratification enables the rapid identification of this population. The end goal of risk stratification is to pinpoint patients that are at elevated risk of an adverse event, like readmission within 30 days of discharge, so that preventative interventions can occur to avoid these adverse events.

Technology can help optimize and automate risk stratification via machine learning, a specific type of artificial intelligence. In practice, AI-enabled risk stratification typically occurs via sophisticated algorithmic analysis of registration, electronic health records, and demographic data to identify individuals that may soon experience an adverse event and would benefit from care team intervention.

This algorithm is developed using a combination of a care team's input, clinical expertise, and patient data—including social determinants of health, biographic data, and patient outcomes.

For a care manager, the use of risk stratification AI tools will represent time savings in clerical or administrative tasks, allowing the care team to focus on more complex patient needs or critical projects. AI tools could also leverage data and outcomes to prioritize patients by the urgency of intervention. In the instance of a patient that is at risk for 30-day readmission, AI can optimize the care manager's workflow in a variety of ways throughout the patient’s episode of care - from the discharge process to supporting the patient at home. AI-enabled tools can automatically send the care manager time-appropriate and data-backed suggestions on tasks to provide education or supplies that the patient may need prior to discharge. Following discharge, AI can help keep the care manager and patient coordinated by automatically implementing an at-home communication protocol to coordinate follow-up care and ensure that the patient has the required supplies to support recovery at home.

AI-enhanced scheduling can support a care manager by automatically scheduling follow-up care at an appropriate time, with the appropriate level of care and place of treatment, and even provide an optimal route for a patient to arrive at the appointment. Automated follow-ups via AI chatbots or text reminders can help ensure patient compliance with a recommended course of care. If a care manager cannot reach a patient, AI can even help support a care manager by identifying ideal communication preferences.

Machine learning quickly evolves to become more accurate at predicting outcomes because it uses actual, real-time data as an input to predict new values; as the program is utilized, it will become more accurate and can better anticipate patient needs and support clinician decision making. Machine learning will also reduce administrative tasks for care managers and teams so that they are equipped to make optimal clinical decisions. Reduction in administrative tasks will free up additional time for clinicians and care managers to deepen patient outreach for high-value patient care tasks and drive a highly satisfied member experience.


4. Social Determinants of Health

Survey data has shown that 80% of health outcomes are non-clinical in nature while only 20% are clinically attributed factors. Investments in technology, tools, and processes for care managers and social workers to access and screen patients for social factors can be a critical component in improving health outcomes. The Covid pandemic created a new sense of priority for the general population where the most vulnerable may prioritize planning for their next meal, shelter, or transportation rather than their next surgery or outpatient procedure.

In order to provide more accessible communication options, payers may need to enhance their investments and partnerships with local community resources. Care managers will need to work more closely with providers to gather and address social determinants of health (SDoH) needs via various screening tools, including screening patients for non-clinical risk factors during their primary care visits or inpatient admissions.

Care management is trending from a ‘single person care gap closure model’ to a ‘population health delivery system model’, leveraging a community based model of care where caregivers and health systems are supported by local communities striving to improve socioeconomic conditions for their populations. Care managers have an opportunity to infuse themselves more in local communities to better understand resources available to their members to meet basic care needs like food banks, shelters, transportation services, and substance abuse services. The expectation is that a happier and healthier population will drive improved health outcomes, particularly in the Medicare and Medicaid populations.

Health plans may also consider investing in technology solutions that execute complex algorithms with member claims, clinical, and SDoH data. It would be valuable for care managers, and ultimately their members, to create a digital ‘Health Hub’ encompassing SDoH member data at both the individual and community levels to stratify in a risk band and quantify the overall risk for various factors. These factors would include ones related to physical (food, transportation, housing), socioeconomic (education, employment, income, social status), and behavioral (safety, diet, exercise, habits) environments. The goal would be to better align care plans by connecting the members to the right resources at the right time, resulting in improved health outcomes.


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