From providers to analysts, predictive analytics streamlines workflows and helps teams improve outcomes.
Predictive analytics is revolutionizing many fields, and healthcare is no exception. It’s a key component for success across teams, enhancing patient care and improving operational efficiency for data-driven organizations. Predictive analytics can also improve workflows and deliver insights that drive better health outcomes. Here, we’ll delve into the role of predictive analytics in healthcare, outline the benefits for different stakeholders, and provide solutions for possible challenges.
What is predictive analytics?
Predictive analytics combines various statistical modeling, data mining, and machine learning techniques. True to its name, its primary goal is to analyze current and historical data to predict future events. Predictive analytics applies to many types of healthcare data, including structured data like electronic health records (EHRs) and unstructured data such as clinical notes and imaging reports. Moreover, it leverages big data to provide insights into patient care, resource management, and health outcomes.
IT operators, care managers, and other healthcare professionals use predictive analytics to surface relevant opportunities and create efficiencies across their organizations.
How predictive analytics works in healthcare
The journey of predictive analytics in healthcare starts with data collection. This involves gathering information from various sources. Some sources include proprietary structured and unstructured data, public health records, and cutting-edge technologies like wearable devices. The data then undergoes rigorous preprocessing and cleaning, ensuring its accuracy and relevancy. Modeling is the next crucial phase, utilizing algorithms tailored to specific healthcare needs. The final stage is the interpretation of results, which greatly influences decision-making and policy formulation.
Predictive analytics empowers users, whether they’re on the front lines of care or analyzing data in an office. For example, a provider may receive a list of helpful questions for the at-risk pre-diabetic patient sitting in front of them. However, a data scientist may build a model that transforms care delivery in a hospital’s oncology unit. Next, we’ll look at how predictive analytics capabilities play out across different roles.
7 teams across the care continuum that benefit from predictive analytics
1. Patients and members
Predictive analytics is pivotal in revolutionizing patient care. It helps patients get the right care at the right time. Predictive analytics also power early reminders and help providers encourage preventive care. It serves the following key functions that benefit patients and members, among others:
- Patient stratification: Predictive analytics enables early diagnosis and preventive care by identifying health risks before they escalate. Care managers and clinical teams can create treatment plans tailored to individual patient profiles that use predictive models. For chronic condition management, these analytics are indispensable, offering proactive approaches to patient care.
- Automated patient messaging: Predictive analytics enables automated messaging, tailored to the specific audience that will receive it. By crafting and sending thoughtful, dynamic content, healthcare teams can promote preventive care (screenings or vaccinations) and engage patients so they’re more likely to receive continuous, regular care.
2. Care managers and liaisons
Care managers use predictive analytics to allocate time and resources where needed most. This strategy enhances patient engagement and satisfaction by ensuring care is timely and efficient. Using predictive analytics to stratify risk, care managers can better target interventions, significantly improving outcomes. Predictive analytics enables better care management in two critical ways:
- Registry and care gap lists: Predictive analytics helps identify polychronic patients (those with three or more chronic diseases), immediately surfacing them so healthcare organizations can offer them additional care.
- Care management: Predictive analytics helps identify polychronic patients (those with three or more chronic diseases) by immediately surfacing them. Then, healthcare organizations can offer those patients more care.
3. Point-of-care clinical teams
Clinicians face immense pressure to choose the right action or diagnosis amidst a sea of choices and possibilities. Predictive analytics helps ease some of this burden. Chiefly, predictive analytics helps providers, as they’re face-to-face with patients:
- Point of care decision support: For clinical teams, predictive analytics is a beacon in the fog of complex decision-making. It augments diagnostics with advanced machine-learning algorithms and proves invaluable in managing emergencies through real-time data analysis.
4. Coders and billers
Predictive analytics helps coders and billers refine their processes. They can recoup wasted time and create lean, efficient workflows. It also helps measure risk, quality, and fraud in the following way:
- Clinical documentation improvement: In the administrative realm, predictive analytics streamlines coding and billing processes, enhancing efficiency. With the right tool, coders and billers can zero in on the information that matters most, so patients’ risk is comprehensively documented. It also plays a crucial role in fraud detection and billing error risk assessment. Moreover, it ensures adherence to regulatory standards, thereby elevating the quality of healthcare services.
5. Analysts and actuaries
Analysts and actuaries benefit from the ability to render complicated insights as data visualizations. They can surface insights throughout their organization and plan for possible outcomes. This helps them ensure sustainable performance in the following ways:
- Medical economics research and contract design: For analysts and actuaries, predictive analytics serves as a compass for long-term strategic planning. They can leverage it to assess financial risks and sustainability, ensuring a balanced approach to healthcare financing.
- Data visualization and dashboard design: Analysts and actuaries can leverage predictive analytics to distill their valuable insights into shareable images and dashboards so that other stakeholders can better understand performance and goals.
6. Data scientists and clinical researchers
For data scientists and clinical researchers, predictive analytics is key to new discoveries, enabling more thoughtful studies and sharper insights around central questions:
- Research and model design: Predictive analytics has led to significant breakthroughs in healthcare, bolstering the development of novel treatments and drugs. Data mining and pattern recognition, essential for drug development and clinical trials, are heavily reliant on predictive analytics.
7. IT operators
IT operators empower their organizations with predictive analytics by building the infrastructure for accessible, trustworthy data. In structuring and organizing an organization’s data properly, they enable other users’ predictive analytics capabilities with reliable, vetted information:
- Health data ETL: IT operators use predictive analytics to maintain data quality and security. Predictive analytics helps IT operators extract, transform, and load data safely and with integrity.
- Data warehouse: For predictive analytics to work properly, great infrastructure and data governance is a must. A data lakehouse allows an organization to access its data in many forms, whether that’s unstructured and raw (for a data analyst) or very refined (for a provider at the point of care). Features like native deletions and offset management mean that users of predictive analytics down the line don’t need to worry about an invalid data point spoiling the accuracy of a dataset.
Challenges and ethical considerations
Despite its many benefits, predictive analytics in healthcare is not without risks and challenges. Data security and privacy remain top concerns, given the sensitive nature of health information. The accuracy and reliability of predictive models are under constant scrutiny to avoid potential misdiagnoses, biases, or treatment errors. Furthermore, ethical considerations such as addressing healthcare disparities and mitigating bias in data and algorithms are critical areas that need ongoing attention.
This doesn’t necessarily spell doom for predictive analytics in healthcare. Still, it does mean that users need to approach any technology with a clear sense of when they’re appropriate, how to avoid missteps, and how to read results with a critical eye.
The future of predictive analytics in healthcare
The future trajectory of predictive analytics in healthcare is both promising and challenging. Emerging technologies like artificial intelligence (AI) significantly augment predictive analytics capabilities. Success in predictive analytics requires the right foundation, and a sophisticated architecture (like a lakehouse) is the first step in leveraging this technology. The integration of these technologies into healthcare workflows is vital for realizing their full potential and improving patient outcomes and operational efficiencies.
Predictive analytics impacts every facet of the healthcare continuum. As a powerful tool for enhancing healthcare delivery, it offers insights that are instrumental in driving efficiency, accuracy, and improved patient outcomes.
Healthcare providers will have to embrace this technology to stay at the forefront of healthcare innovation. Predictive analytics opens up exciting possibilities, from earlier interventions to a more customized, thoughtful, engaging patient experience.
With the right mix of thoughtfulness and innovation, technology advocates and healthcare pioneers can take a collaborative approach toward a more predictive and proactive healthcare system.