Competency Appraise: Multiple Methods Of Data Resources And

Competencyappraise Multiple Methods Of Data Resources And Data Collect

Identify big data sources and types for your health system population health management program that was introduced in the module 01 summative assessment. Then describe what specific various data elements are needed to help providers make immediate gains in patient well-being while developing best practices for future initiatives.

Assess how learning to use more readily available data, like demographics, ICD-10 codes, and ADT alerts, will be a vital first step for eventually integrating much more complex and varied big data into the population health management ecosystem.

Paper For Above instruction

Effective population health management (PHM) relies heavily on the strategic collection and utilization of big data from a multitude of sources. These data sources are instrumental for formulating a comprehensive understanding of patient populations, facilitating proactive interventions, and improving patient outcomes. In the context of a health system's PHM program focused on diabetes management—a prevalent chronic condition affecting diverse populations—the identification and integration of appropriate data sources are vital for success.

Big Data Sources and Types for the Population Health Management Program

Primarily, big data sources in PHM encompass electronic health records (EHRs), claims data, laboratory and pharmacy data, wearable device outputs, and public health databases. EHRs form the backbone, offering rich clinical data such as diagnoses, lab results, medication lists, and clinical notes (Kellermann & Jones, 2013). Claims data provide insights into healthcare utilization, costs, and billing patterns. Laboratory systems contribute detailed biochemical and diagnostic information critical for disease monitoring. Wearable devices, increasingly prevalent, generate real-time data on physical activity, blood glucose levels, and heart rate, which are valuable for continuous patient monitoring (Shapiro et al., 2020). Public health data, including state immunization registries and socioeconomic data sets, contribute contextual information that influences health outcomes.

Integrating these data types allows healthcare providers to develop a multidimensional portrait of the patient, encompassing clinical, financial, and social determinants of health. For example, combining EHR and claims data can reveal patterns of healthcare utilization that signal poorly managed diabetes, such as frequent emergency department visits or hospitalizations (Venkatan et al., 2019). Laboratory data enhances the understanding of glycemic control, while wearable device data can inform real-time management adjustments. Public health data adds vital context regarding social health determinants, such as residential zip codes associated with food deserts or environmental hazards.

Specific Data Elements Needed for Immediate and Future Gains

For immediate improvements in patient well-being, certain critical data elements are essential. These include demographic information (age, gender, ethnicity), clinical diagnoses (ICD-10 codes), medication adherence data, recent lab results (e.g., HbA1c levels for diabetics), and recent encounters or hospital admissions (ADT alerts). For example, monitoring HbA1c levels allows clinicians to adjust treatment plans promptly, reducing the risk of complications (American Diabetes Association, 2022). Demographic data facilitate targeted outreach to vulnerable populations—such as low-income or minority groups—who may face barriers to care.

Additional data elements supporting future initiatives include socioeconomic status indicators, mental health status, behavioral health data, social determinants like housing stability and food security, and data from social service agencies. Incorporating medication refill data helps identify adherence issues that require intervention. Encounters with Emergency Departments or readmission rates provide measures of the effectiveness of current management strategies.

Examples include integrating socio-economic data to customize community-based interventions or using social determinants data to identify patients at risk of poor outcomes due to environmental factors. Involving comprehensive social data allows for a holistic approach, combining clinical management with social support services (Harvard T.H. Chan School of Public Health, 2018). Together, these data elements enable providers to craft personalized care plans, improve resource allocation, and develop predictive models to forecast future risks.

The Role of Readily Available Data in Building a Population Health Ecosystem

Learning to utilize readily accessible data such as demographics, ICD-10 codes, and Admission-Discharge-Transfer (ADT) alerts is a fundamental first step toward integrating more complex big data sources. Demographics are basic but highly informative, enabling the identification of at-risk subpopulations and facilitating population segmentation (Buntin et al., 2011). ICD-10 codes provide standardized diagnosis information, enabling precise tracking of disease prevalence and comorbidities necessary for effective management and risk stratification (World Health Organization, 2019).

ADT alerts offer real-time notifications about patient admissions, discharges, and transfers, which are crucial for care coordination, timely follow-up, and avoiding redundant testing (Liu et al., 2022). The integration of these sources provides a foundation for more sophisticated data analytics, including predictive modeling and machine learning applications. These initial data points are relatively easy to collect, analyze, and interpret compared to more complex data such as genomic information or wearable device streams, making them valuable steps in building a scalable population health management infrastructure.

Overall, mastering the use of these accessible data elements creates a culture of data-driven decision-making and paves the way for incorporating complex datasets that can further enhance patient care, resource planning, and health outcomes (Grossman et al., 2020). Consequently, healthcare organizations can gradually develop an integrated data ecosystem that supports continuous quality improvement and personalized medicine.

Conclusion

In conclusion, the foundation of successful population health management lies in identifying and effectively utilizing diverse big data sources and elements. By leveraging EHRs, claims, laboratory, wearable, and public health data, healthcare providers can develop a nuanced understanding of their patient populations. Incorporating essential data elements like demographics, ICD-10 codes, lab results, and ADT alerts enables immediate clinical improvements and supports the development of future best practices. Emphasizing the mastery of readily available data forms an essential stepping stone toward integrating more complex data sources, ultimately fostering a comprehensive, data-driven approach to improving population health outcomes.

References

• American Diabetes Association. (2022). Standards of Medical Care in Diabetes—2022. Diabetes Care, 45(Supplement 1), S1–S232.
• Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The Benefits of Health Information Technology: A Review of the Recent Literature Shows Predominantly Positive Results. Health Affairs, 30(3), 464–471.
• Grossman, L. V., Farag, N. H., & Medlin, C. (2020). Building a Data-Driven Population Health Ecosystem. Journal of Healthcare Management, 65(2), 120–132.
• Harvard T.H. Chan School of Public Health. (2018). Social and Behavioral Determinants of Health. The Public’s Role in Health Promotion. Harvard University.
• Kellermann, A. L., & Jones, S. S. (2013). What It Will Take To Achieve The As-Yet-Unfulfilled Promises Of Health Information Technology. Health Affairs, 32(1), 63–68.
• Liu, C., Lee, H., Okon, T., & Bian, J. (2022). The Effectiveness of ADT Alerts in Improving Patient Outcomes. Journal of Medical Systems, 46(4), 52.
• Shapiro, M., Laxmisan, A., & Arora, S. (2020). The Role of Wearables in Population Health. Journal of Healthcare Informatics Research, 4(3), 332–341.
• Venkatadass, K., Bing, H., & Lee, S. Y. (2019). Integrating Claims and Clinical Data for Enhanced Population Health Management. Journal of Managed Care & Specialty Pharmacy, 25(10), 1149–1155.
• World Health Organization. (2019). ICD-10: International Statistical Classification of Diseases and Related Health Problems (10th Revision). WHO Press.