6 Reasons Why Predictive Analytics are the Future of Healthcare

Healthcare powered by predictive analytics is transforming the healthcare space. From enabling more accurate and proactive care delivery and better patient outcomes to streamlining provider processes and workflows. And with continued advances in machine learning, and healthcare technologies constantly honing the accuracy of predictive models and algorithms, it’s clear that the future of healthcare will be driven by predictive models.

Predictive analytics can shift care approaches to become more proactive, including:

  • Better inform and guide care decisions with real-time patient data and 360-views
  • Streamline care delivery models with preconfigured risk notifications and tasks
  • Identify patient behavior patterns and care gaps
  • Adapt and account for social determinants of health and address healthcare disparities
  • Seamlessly connect patients to community resources and across entire care continuum
  • Improve operational efficiency to reduce staff burnout, increase focus on care

Why Are Predictive Analytics Important?

Predictive analytics are used across many industries. Although use cases vary, the general principle is the same: predictive models are “trained” by ingesting and processing massive amounts of data from many sources, quickly identifying patterns, and generating meaningful insight to inform business processes, such as inventory purchasing in retail, equipment maintenance scheduling in manufacturing, or fraud prevention in banking.

When leveraged in a healthcare setting, predictive analytics can shift a healthcare organization’s approach from reactive to proactive delivery of care.

Software that integrates and analyzes historical and real-time patient data sources can:

  • Provide both a current-state and future-state health status view of patients
  • Predict the onset of chronic and infectious diseases, adverse events, and the likelihood of hospital readmissions
  • Enable care managers and clinical team members to quickly take preventative action and recommend preventive interventions
  • Streamline patient workflows and care delivery processes for more accurate, optimized patient care and a healthcare workforce that’s more efficient and balanced
  • Help care managers quickly adjust and adapt care plans
  • Leverage real-time insights from remote patient devices like smart watches, health apps and wearables

But to fully leverage the benefits of predictive analytics in healthcare, organizations need a strategy that addresses common challenges: scattered data, siloed care systems, interoperability issues, and disparate point solutions.

The Benefits of Leveraging Predictive Analytics in Healthcare

Predictive analytics can be applied to guide decisions across the healthcare ecosystem. Some of the results of using those insights include improving population healthcare management, better patient outcomes, greater efficiency in care delivery, and reducing the costs associated with readmissions and unnecessary interventions.

Here are six different ways predictive analytics can provide immediate and long-term benefits for healthcare organizations.

1. Better Inform and Support Care and Treatment Decisions

Data informs decisions, and healthcare professionals make a lot of decisions with high-stakes outcomes. Predictive analytics offer three key benefits when it comes to the care continuum.

First, they can help ensure diagnoses, care decisions, and treatment choices are made more accurately, faster. A software solution that uses predictive analytics with intelligent algorithms that can process enormous quantities of diverse data sets to generate insights for quicker, more accurate diagnoses, care decisions, early interventions, and treatments. In doing so, care management teams can provide the right treatment, to the right patient, at the right time.

Second, leveraging predictive analytics can also help healthcare providers be better prepared to treat patients who are at a high risk for serious conditions by predicting high-risk factors. For example, a 2021 study published in the Journal of the American Medical Association found that predictive analytics using machine learning algorithms helped identify demographic characteristics and certain comorbidities in patients that increased their risk of developing severe COVID-19 complications.

And third, if a medical management software solution also has built-in patient health assessments, the integrated predictive analytics technologies can use these to help care managers continuously adapt and evolve care plans in real-time, as well as solve care issues faster. While critical touch points like in-person assessments will never lose value, leveraging analytics-powered technology and digital assessments can position healthcare staff to provide better, faster care to patients.

2. Identify Patterns in Patient Behaviors

Predictive algorithms can also quickly sift through historical and real-time data to identify adverse patterns of behavior, such as routinely missing appointments or failing to fill medication prescriptions.

Identifying these behavioral patterns is key for two reasons.

  1. It lets care managers know when to reach out to patients and try to find out why a patient is not getting the care they need. This could be due to an associated social need – such as no access to transportation to pick up medications or attend appointments. Or, it may be the patient forgets and the care manager learns they need help organizing their health appointments.
  2. It gives care managers a clearer picture of how to provide better care and more effectively engage members. For example, analytics may help reveal that a patient needs multiple appointment reminders, help with transportation to appointments, medication delivery, or assistance scheduling alternate times.

Identifying and understanding patient behavioral patterns allows care managers to adapt care plans and solve care issues and obstacles in real time.

3. Factor in SDOH Data to Predict and Address Health Risks and Obstacles

As noted above, SDOHs significantly impact a person’s health. Integrating and analyzing SDOH data gives care managers a more complete picture of a member, surfacing non-clinical factors that can affect health outcomes.

For example, a member may lack transportation and have difficulty getting to appointments, or data points on neighborhood characteristics like walkability, availability of basic resources (e.g., grocery stores), and air pollution levels could help predict if a patient is at higher risk for chronic conditions such as cardiovascular disease. These kinds of predictive elements can help surface members who may need SDOH support and assistance.

When a medical management solution enhances member profiles with this kind of information, care managers can intervene and connect members with community resources that help them live a healthier, higher-quality life at home. This helps health plans to identify and close critical care gaps and better remove health-related social challenges.

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4. Create a Safer Home Environment to Prevent Hospital Readmissions

Predictive analytics can draw on data from a combination of sources like a member’s health records, historical use of emergency services, and remote patient monitoring devices (including smart watches and other wireless wearables like hearing aids) to predict which members are at higher risk for preventable events that can lead to re-admission.

Automated workflows can then push notifications in the care management platform. From these, a care team can reach out proactively, enact preventative measures, and help a member avoid complications and re-hospitalization.

For example, fall-risk pendants can identify members who may be prone to falling in the home after discharge. This data gets fed into the medical management system and analyzed by the algorithms in place. A care manager receives a risk notification to proactively reach out and schedule a virtual chat to discuss preventive and safety measures. Months later, the member’s profile shows a reduced risk of falling.

5. Reduce Healthcare Staff Burnout, Increase Focus on Care

Leveraging predictive analytics can also help reduce some of the administrative burden that leads to burnout among healthcare professionals. Smart technologies, with automations and data-driven features, can help physicians extract relevant patient information faster.

Automating patient throughput and workflows can help healthcare professionals focus their time on patient care. Instead of being bogged down by inefficient care management systems or disconnected data, medical professionals on the patient’s care team can stay focused on the all-important human interactions with members.

Moreover, leveraging predictive analytics can help healthcare professionals be more effective by offering quick and accurate patient information as well as predictive analytic recommendations for treatment. This enables a physician to focus their care efforts and more proactively approach the treatment of patients for better short- and long-term outcomes.

6. Improve Patient Engagement and Satisfaction

Health outcomes are also dependent on the patient – specifically positively engaging them to be an active participant in their health care plan.

Equipped with analytics insights (and more time to focus on members, thanks to increased efficiency from automation), care managers can develop and maintain better relationships with members. They can design more personalized care plans, better understand and address individualized impacts of SDOH, and provide targeted outreach and resources focused on individual needs and lifestyles.

And with better-informed care and treatment decisions comes a deeper level of trust between patients and their care providers, which in turn leads patients to be more likely to be engaged and have better long-term health.

How Can You Successfully Leverage Automation and Predictive Analytics?

Predictive healthcare models can help payers and providers drive more accountable, value-based care. The technology opens a window for everyone involved in a member’s care to see real-time developments, shedding light on possible future events rather than relying solely on historical data.

But in order to access all of the above benefits, it’s important to have the right medical management platform in place.

From our perspective, the best technology doesn’t try to reinvent healthcare. Rather, it positions organizations to reinvent how they deliver healthcare, providing payers and providers with a single solution with all the tools and capabilities they need to put innovative ideas into action for optimized care management and delivery.

The way to successfully leverage automation and predictive analytics is to choose a medical management platform solution that aligns your care management teams, UM teams, providers, and payers around member information in one place.

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