Due to the rising costs of healthcare, providers are focusing on prevention as a treatment. They understand the indispensable benefits of preventive healthcare.
Predicting risk factors to prevent illness is the key to maintaining good health. Waiting until after a diagnosis makes treatments more complex and increases healthcare’s financial responsibility.
The shift from reactive to anticipatory health services requires leveraging data and digital technologies. That is the key to reducing the burden of chronic illness and improving overall patient outcomes. Data analytics can optimize efficiency and enhance healthcare delivery systems.
It’s All About Trends
Preventive medicine allows for the deterrence or early treatment of disease. Predictive analytics is key. It enables researchers to identify patterns and see trends as they emerge.
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Identifying trends also allows practitioners to spot patients at risk for certain chronic illnesses and make informed treatment decisions. Through predictive analysis, physicians can diagnose illness more accurately and determine the need for resources for their patients and their practices.
A cornerstone of this process is electronic health records (EHR). They provide thorough information collection as the primary data source in predictive analysis. Analyzing this data can predict future health risks, prevent potential complications, and establish comprehensive treatment options.
Access to extensive data is often a barrier for providers. However, EHRs and technological advances allow for data-sharing and analytics between providers and care team members. When everyone has the same information, they can better coordinate, reduce redundancies, and assign actionable tasks.
Predictive analysis can:
- Spot a patient’s early decline and provide insight into the cause.
- Integrate risk scores into patient health records.
- Create intelligent reports tailored to a practice or patient’s needs, considering lifestyle factors, genetics, and environment.
- Allow clinicians to identify patients at risk for chronic illnesses such as diabetes and heart disease and take preventative action.
Data analysis is changing healthcare to reduce costs and improve efficiency.
Types of Data
Data comes in many forms, starting with genetics. By analyzing genetic information, researchers can develop tests that anticipate the probability of someone developing a disease. Genetic data also helps to identify individual biomarkers for illnesses. This approach allows researchers to identify high-risk groups using factors such as age, race, environment, health, and gender, which improves screening tests.
Data collection is individualized, too. Physicians might use patients’ wearable devices to help gauge overall health and allow them to predict their practice needs. For example, what inventory should they keep on hand at the office? How many minutes, on average, do they need for each patient? What patients need specific screening tests?
Leveraging Data to Improve Patient Engagement
Data analytics can play a significant role in getting patients involved in their healthcare needs. Giving patients access to their data makes them active participants in their health and provides better communication with care providers.
Some tools that can enable this approach include:
- Patient portals—Patients can view test results online, communicate with their healthcare provider, and access health education resources. A 2021 study published in the Journal of Medical Internet Research found that patient portals enhance the doctor-patient relationship.
- Wearable devices – Wearable devices do more than just count the steps someone takes daily. They can automate medication refills, ensure treatment compliance, and provide encouragement. Wearable devices can send data directly to providers and help them monitor patients outside the office.
- Online resources such as chatbots and blogs
Doctors can study data to understand patients’ questions and determine better ways to help them manage their health.
Advancing Technology with Machine Learning (ML) and Artificial Intelligence (AI)
AI can help providers analyze patient data and make improvements. Predictive analytics using AI and ML can deliver insight into how a patient may respond to a specific medication and identify patients at risk for infection or hospitalization, for instance.
AI systems can also reduce the time physicians spend analyzing patient data. A 2021 study published in Health Informatics found that doctors spend 62% of their time per patient reviewing their electronic records. AI can ease that burden, reduce fatigue, and provide better preventative care. The technology can analyze documents and highlight critical information.
AI allows for better automation and more efficient office practices. It can automate touchpoints such as appointment scheduling and check-ins. Chatbots can analyze questions and recommend patient education resources. Data from these processes can supply information on patient concerns and additional resources they might need.
The Expanded Role of Predictive Analytics
Predictive analytics is a common factor in all areas of preventative medicine. The combination of advanced statistical analysis, data mining, AI, and machine learning helps forecast the future for patients and the healthcare industry.
Leveraging data will:
- Improve outcomes by identifying patients at risk and enabling early intervention.
- Delivering personalized treatment plans improves the quality of patient care. Predictive analysis examines individual health and past outcomes, creating more comprehensive care plans.
- Allow for data-driven decisions by healthcare organizations, employers, researchers, and practitioners.
- Enhance community well-being and identify health risks such as water contamination.
The healthcare industry is embracing data to navigate the complexities of a changing world. As a result, data analytics is revolutionizing preventative medicine and shaping the future of patient care.
