Today business intelligence and data analytics are the primary technologies that enable Population Health Management.
Population health management (PHM) is a healthcare methodology that describes and enables care delivery across a population or a group of individuals.
To achieve the goals of Population Health Management, the clinical, financial, and operational data from the overall organization must be brought together and actionable steps for providers via analytics, including population health data analytics and Predictive analytics.
Population health analytics technologies and effective programs will provide real-time insights, allowing providers to identify and address any care gaps within the patient population. This enables a healthcare organization to improve patient outcomes while also saving money by predicting individual healthcare journeys to deliver person-centered care and the costs involved.
The data gathered during the Population Health Management (PHM) analytics phase informs ‘what' interventions are implemented to ‘who' in the population and ‘how' this will be measured.
Aside from the variety of data required for this method, big population health care data are distinguished by high volume, high velocity, and inconsistent data flows.
Therefore big data continues to grow in size. The specialized tools and analytical methods required to extract valuable insights from Big Data sources revolutionize big data's use in public health. These specialized technologies are referred to as “big data analytics.”
Using big data analytics to improve public health can help with research, surveillance, and intervention. As a result, it can help to design and implement more effective, evidence-based public health policies.
RESEARCH: Allows precise identification of at-risk populations in a better way of understanding of human health and disease through population health solutions, including the interaction of genetic, lifestyle, and environmental determinants of health;
SURVEILLANCE: Allows for improved surveillance of communicable and non-communicable diseases; and
INTERVENTION: Improves health promotion and disease prevention through more targeted strategies and interventions.
Implementing big data to help, manage and evaluate population health analytics has resulted in several healthcare improvements. The population health management, reporting, and evaluation processes generated additional data that, when analyzed, helps to improve program implementation and quality.
This population health management framework, which incorporates Big Data, determines which focus areas best fit the new managerial and population requirements. Such development methodologies generally use data to find ways to improve the quality, efficiency, or equity of care provided. It usually consists of:
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Identifying unjustified variation in the system
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Analyze duplication
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Bridge the Gaps
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Quadruple Error analysis where events in the population are high cost, low quality, represent a poor patient experience and contribute to increased inequalities.
Therefore, the professionals of population health, fraud, and errors can be easily detected and prevented when using big data and predictive analytics, saving healthcare organizations a lot of money. There are already several big data solutions and analytics solutions available to assist providers in preventing such frauds and human errors, particularly when it comes to dosage.
CONCLUSION:
Big data has the potential to entirely transform population Health management. To improve patient outcomes, reduce costs, and increase efficiency across all departments, the incorporation of Big Data is the need of the hour.
Perhaps more importantly, big data will assist clinicians and hospitals in providing more targeted healthcare and achieving better results. Big data is a driving force for pharma companies, allowing them to design and build more innovative drugs and products.
Thus entire healthcare stakeholders can rely on population health management, big data, and predictive analytics to address significant readmission rates, high-risk patient care, staffing issues, dosage errors, and other issues.
