The Map of Healthcare Analytics Domains

As the landscape of healthcare continues to embrace technology, healthcare analytics has turned up at the forefront of business priorities for healthcare organizations.

The healthcare analytics business is poised to witness remarkable growth in the coming years. This science refers to the extraction of insights that contribute to creating ideas for quality improvement, and systematic growth. Healthcare analytics is a vast domain, and here is a breakdown of each field that comes under its umbrella.

Predictive Analysis uses technology and statistics to scan through volumes of information, employ data mining, and arrive at an analysis that can predict outcomes concerning patient treatment. This form of analytics needs patient data that includes treatment history, related medical research, geographical implications, etc. that are then used to form learning modules and make predictions.

As more and more data is generated, healthcare organizations need to leverage data for enhanced decision-making. Data visualization tools allow them to analyze the quality of care delivered and operational efficiencies. Advanced BI software can provide insights achieved through this analysis of the volumes of data.

Remote Patient Monitoring is bringing a paradigm shift in the care delivery process. There is a shift from hospital-centric care to personalized care within the comfort of your home. This makes it more affordable to the vulnerable sections of society. Providers with the help of modern technology like AI and analytics can improve the quality of treatments and care delivery. It can also support an easy and quick collection of health data from patients remotely.

Data-driven patient engagement helps providers measure the value of their caregiving. It provides healthcare providers with crucial insights into the customer’s life, helping them in actual decision making to actively engage with the patients. Providers can analyze data from satisfaction surveys, track the number of scheduled appointments, patient behavior, and more.

Healthcare Informatics is used to describe the process of gathering and applying healthcare information to increase collaboration among various healthcare providers associated with a particular patient, along with relatable pharmaceutical and R&D data. The meaningful analysis of this data is a significant leap towards healthcare reform.

Analytics used towards population health management is the process of gathering data of a specific population and forming reasonable conclusions that can manage certain diseases related to that population. The analysis of the gaps in providing care to the patients improves outcomes and generates revenue. It further helps in the evaluation of preventing chronic diseases by offering insights into the causes and preventive measures.

Applying analytics to practice management allows for the access of data to rectify processes and take proactive versus reactive steps toward operational efficiency. Adopting a data-driven management attitude with a particular focus on patient behavior and sentiment data will assist in aligning staff and physicians toward decision-making, and improve the overall health of the practice.

Payer organizations continue to struggle with margin pressures, competition, connectivity issues, and more. Payer administration analytics considerably enhances efficiency by offering customized and meaningful compliant customer communication to improve administration management, claims processes, billing processes, enterprise workflows, and more.

Through the use of healthcare data analytics, fraud, abuse, and wastage can be mitigated to a large extent. Detecting the threats and preventing it is a priority for payers today. Application of analytics will increase adjudication and payment integrity. It can further recover savings, improve efficiency, and ensure regulatory compliance.

Containment of cost is a weakness that is prominent across most of the healthcare organizations. A relatively important strategy of controlling operational expenses and budgetary constraints is imperative. With the help of analytics in data management, unrequired services, fictitious providers, false referrals, illegal kickbacks, and non-billed services can be identified and possibly avoided.

Risk Management is a growing concern for healthcare providers. It requires a shift from a reactive to a proactive approach. Through the use of Electronic Data Warehousing (EDW) solutions, potential risks are identified and averted, along with an analysis of the factors causing the risk, thereby reducing re-admission chances.

Data-driven clinical decision support reduces the chances of poor care delivery. Through the analysis of data, alerts are sent to reduce the possibility of ‘false positives.’ Further, physician acceptance is gauged, and cost-effective measures are evaluated.

Data analytics can increase the efficiency of claim management processes. It can offer meaningful suggestions on improving customer experiences, risk management, operational efficiency and more. By leveraging the data for active decision-making, healthcare providers can improve their ROI considerably. It can further help minimize the ratio of claims rejection, and denial enhancing the health of the entire revenue cycle management system.

When patients data is successfully mined, analytics is applied to make predictions about their satisfaction levels. This further translates into an immediate improvement in quality levels among the staff of the healthcare agency. It consequently affects patient satisfaction, physician comfort, and overall revenue of the organization.

Data gathering and mining have limitless possibilities in the world of medical imaging. Patients can submit their data and scanned images, resulting in reduced costs and in-person clinic encounters. The features can also include activity tracking, physical monitoring, and more, which were previously limited to clinic visits only.

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