Healthcare Data – Enhancing Population Health Management
As value-based care (VBC) concepts and objectives continue to change, healthcare organizations struggle to develop a population health management (PHM) roadmap and track associated costs and income. Along with a constant influx of population health data, the healthcare industry is facing lower margins, reduced budgets, and altered payment arrangements.
Despite the complexities involved in serving large populations, data analytics has been frequently cited as the key to unlocking value in the healthcare industry. Research by Deloitte indicates that 92% of the respondents believe healthcare data transformation-enabled solutions are an essential driving force shaping the current healthcare industry.
Population Health Management (PHM) is a mode of automation in healthcare that focuses on preventative care, improves patient outcomes, reduces costs, and increases access to healthcare for the population at large.
With continual developments in healthcare innovation and healthcare data, the potential for enhancing population health is endless. By leveraging data to identify high-risk populations, healthcare providers and payers can implement targeted interventions that improve health outcomes and reduce costs.
Population Health Management - Steady Data Sources
Despite having unprecedented access to a large pool of population health data, the information is frequently segregated within the organization. Below are the primary sources of population health data.
- EHRs: Electronic Health Records (EHR) make it possible to recognize at-risk communities and examine social determinants, such as poverty or lack of access which impact population health outcomes.
- EMRs: Electronic Medical Records (EMR) data allows payers and providers to have a comprehensive view of patient information that can be aggregated over time and used as part of population health solutions. This helps to find opportunities for preventive care interventions and target groups who need extra support. Additionally, with EMRs, monitoring the success rate of implemented measures is possible - enabling improved quality in care while reducing costs simultaneously.
- Claims: Claims records offer detailed insight into the healthcare needs of a population, providing information on who is accessing services and what kind. By leveraging this data for public health policy-making or to assess risk levels in different areas, payers can ensure that resources are correctly allocated where they will make the most difference – helping those at the highest risk get the treatments needed when it matters most.
- Socioeconomic Data: Socioeconomic data can help healthcare providers and payers by providing insights into patients' social determinants of health. This data can be used to identify underlying factors adversely affecting a patient's health.
- Patient-generated Data: From satisfaction surveys to messages sent through portals and IoT sensor stats such as sleep patterns or exercise levels - tapping into a range of patient-generated data streams, population health management software can gain valuable insights that would otherwise go unnoticed. It allows for real-time risk assessment regarding lifestyle diseases like heart issues.
- Medication Adherence Data: Due to fragmented care across different organizations, patients with multiple medications face a heightened risk of contraindications in population health management. By combining claims data, EHRs, and e-prescribing databases with socioeconomic indicators, healthcare professionals can accurately predict which individuals are more likely at risk for ceasing treatment because of financial constraints – allowing them the opportunity to avoid potentially dangerous situations.
Using Healthcare Data to Innovate Population Health Management
Predictive Analytics and Data Analytics allow healthcare providers and payers to proactively identify and manage the health of populations, rather than just treating individuals on a case-by-case basis. Let's take a look at how population health data helps healthcare professionals provide value-based care.
For Healthcare Providers
- Health Risk Assessment: Primary care records like hospitalization data, prescription data, and insurance claims, allow healthcare providers to look out for specific information on health risks associated with a certain population or demographic. With Machine Learning, AI, and Data Analytics tools, HCPs can create algorithms and models that can identify patterns and trends in the data. This can help to identify high-risk populations and target interventions to reduce health risks.
- Care Management: Predictive Analytics can be used to develop predictive models that can be used to identify patients who may be at risk for certain conditions or illnesses. This can help healthcare providers proactively identify and address issues before they become more serious. This information can be used to create personalized care plans for each patient, allowing for more effective care management.
- Remote Patient Management: Population health data on patient health, medication adherence, and risk for hospitalization allow for remote patient monitoring for healthcare providers. It helps identify patients at risk for readmission or poor outcomes, helping healthcare professionals proactively provide interventions or additional resources to improve patient outcomes.
- Medication Adherence: HCPs can use population health data to enhance medication adherence by measuring and analyzing patient behavior. Three key measures of adherence are persistence, compliance, and dose intensity. Persistence measures how long patients take a drug before switching or stopping, compliance measures how closely patients follow the prescribed treatment plan, and dose intensity measures the amount of medication taken. Data transformation-enabled solutions reduce the cost of data collection and improve its timeliness, while clinical AI can help identify the best channels and times to engage patients to improve medication adherence.
- Personalized Medication: Predictive Analytics can be used to enhance personalized medication by helping to identify individual patient factors, such as age, gender, medical history, and genetic information, that may affect the effectiveness of various treatments. By analyzing data from patient records, HCPs can design more tailored treatments suitable to the individual’s needs. Additionally, Data Analytics can be used to develop predictive models to anticipate a patient's response to a particular medication, allowing for more accurate and timely prescribing.
For Healthcare Payers
- Optimal Pricing: Healthcare Data Analytics can be used to enhance optimal pricing for healthcare payers by providing insights into pricing trends, patient utilization, and competition. Data analysis can be used to identify pricing strategies that balance cost and revenue objectives, tailor pricing to different markets and customer segments, and improve the accuracy of pricing decisions. It also can help healthcare payers identify opportunities to reduce costs and maximize profits.
- High-Cost Claimant Support: By analyzing claims data, payers can identify which patients are likely to benefit from preventative care or disease management programs and set pricing accordingly. This approach can help healthcare payers balance their bottom lines with the needs of their customers with a planned budget and loss prevention.
- Early Intervention and Disease Prevention: Healthcare Data Analytics can enhance early intervention and disease prevention for healthcare payers by using Predictive Analytics to recognize early signs of illness. By analyzing Big Data, healthcare organizations can identify patients with comorbidities who are likely to benefit from early interventions. Furthermore, Data Analytics algorithms can be used to provide a structured overview of extensive research on disease prevention, summarizing state-of-the-art methods. This approach can help healthcare payers reduce costs associated with managing chronic diseases while improving patient outcomes.
- Cohesive Collaboration: Healthcare Data Analytics can be used to understand HCP patterns, identify areas of improvement, and generate insights to improve collaborative relationships with HCPs. Data harnessing allows the identification of gaps in care and develops strategies to improve care coordination and communication. Additionally, Data Analytics can be used to compare HCP performance and help payers identify best practices, which can be shared with HCPs to improve collaboration.
- Predict Recurrence and Prognosis: By integrating patient records with other health data, healthcare organizations can detect warning signs of serious medical events and proactively prevent their occurrence. Predictive analytics can also enable healthcare payers to determine patient journeys and predict the churn time to start with robust financial planning.
Optimizing Population Health Management with Data Transformation
Population health management professionals are recognizing the potential of data-transformation-enabled solutions to revolutionize their services and have taken measures to capitalize on this technology.
Data Analytics is revolutionizing population health management by ushering in a new era of efficiency and precision. Automated data collection, analysis, care delivery processes, and tailored insights to patients create more effective treatments with improved outcomes at reduced costs. Patient engagement also increases through real-time feedback that allows for an enhanced level of personalization - defining the future of healthcare.
JK Tech is a leading global Digital and Business Consulting provider revolutionizing healthcare through cutting-edge Hyperautomation and Data solutions. We partner with our customers to help them reduce costs, improve care, and provide equitable and qualitative outcomes for their patients and members. When designed, architected, and implemented correctly, AI/ML and RPA technologies can dramatically enhance and complement existing processes and solutions for Value-Based Care, Population Health Analytics, and Revenue Lifecycle Management. JK Tech provides expertise, resources, and management to help you achieve your outcomes.