Question

Explain how different levels of healthcare data analysis potentially improve healthcare delivery and/or outcomes from previous layers.

pls, provide details accordingly. thx.

Answer 1

Healthcare data analytics is the collection and analysis of data in the healthcare industry in order to gain insights and support decision-making. From key areas like medical costs, clinical data, patient behavior, and pharmaceuticals, healthcare analytics can be used on both macro and micro levels to effectively streamline operations, improve patient care, and lower overall costs.

Healthcare data is the most complex of any industry. From electronic health records (EHR) to monitoring real-time vital signs, data not only comes from several sources but has to comply with government regulations. It’s a difficult and delicate process and requires a level of security and connectivity only an embedded analytics solution can provide.

As with any industry, a Business Intelligence (BI) and reporting solution can significantly improve operational efficiency, reduce costs, and streamline processes through measuring and leveraging KPIs to spot opportunities and inform decision-making. From patients and providers to payers and suppliers, unlocking the value of data benefits everyone.

As healthcare providers transition from fee-for-service to value-based care models, the need to improve efficiency and care makes data analysis a core function of day-to-day operations. With an embedded analytics and reporting solution, providers can:

Improve performance by delivering data-based quality care.

Reduce patient wait-times by measuring and leveraging scheduling and staffing procedures.

Improve patient satisfaction and quality of care by streamlining tedious processes related to making appointments, processing insurance, and providing referrals.

Provide patients with more personalized treatment and improve overall patient experience.

Reduce readmission rates by leveraging population health data against personal patient data to predict at-risk patient.

High-Risk Patient Care

Healthcare can be expensive and complicated for patients seeking emergency services. While the costs increase, the patients don’t always enjoy better outcomes, so a significant change is needed in the processes of hospitals.

With digitized healthcare records, patient patterns and histories can be identified more efficiently. Predictive analytics can isolate patients that may be at risk for crisis situations from chronic health problems, giving physicians an opportunity to provide corrective plans that decrease emergency visits. Monitoring these patients and offering customized care solutions is impossible without sufficient data, so the use of a Business Intelligence (BI) solution in healthcare is of paramount importance to protecting high-risk patients.

Patient Satisfaction

Patient satisfaction and engagement are a concern for many healthcare facilities. With wearables and other health tracking devices, physicians can take a more active role in preventative care for patients, and patients can become more aware of their role in their own health. This knowledge not only improves the relationship between physicians and their patients but also lowers hospitalization rates and addresses severe health concerns that could be prevented.

Human Error

Many preventable health concerns or insurance claims are a result of human error, such as a physician prescribing the wrong medication or the wrong dosage. This not only increases the risk to patients but also increases the insurance costs and claims costs healthcare facilities absorb.

A BI platform can be leveraged to analyze patient data and prescribed medication to corroborate data and alert users to unusual prescriptions or dosages to reduce human error and prevent patient health concerns or death. This is especially helpful in fast-paced environments where physicians handle multiple patients in the same day, which is a situation ripe for errors.