How to be Successful in Healthcare Analytics (Part 2)
By: Kashika Rathore
Tags: Healthcare Technology, Data Visualization, Data, Health, Data Science

(Image by RACGP)

With the recent hit of COVID-19, producing a surge in patient demand and shortages of medical supplies, hospitals desperately need a grasp on their analytical services to ensure optimal clinical results. Health Catalyst is the founder of the nine-step Healthcare Analytics Adoption Model, a guide on how to implement an analytically-motivated framework. We have gone through the first five levels in the previous article, which iterated the processes behind the collection and management of big data. The remaining four extend to policy formulation and tailoring patient care. If you haven’t already, make sure to check out part one before reading any further!

The levels of the Healthcare Analytics Adoption Model

Level 5: Waste and Care Variability Reduction

“ Waste production and internal optimization”

Building upon the preceding levels, the data governance function in Level 5 has expanded to better individual patient care, minimize waste, and reduce variability. There are multidisciplinary teams available to oversee opportunities for improving health and financial wellbeing. Recall in Level 4, the EDW was developing patient registries and providing consistent internal/external reports throughout an enterprise. Now, the EDW is “organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries.” The registries continue to become more precise, the data content now covering data from labs and pharmacies.

Level 6: Population Health Management & Suggestive Analytics

“ Tailoring patient care based upon population metrics.”

Health systems at this level have already cultivated a data-driven mentality and successfully adopted a data-driven foundation. Bundle payments, a payment model which requires providers to assume risks for value-based care, manage about 50% of acute care cases. The healthcare model supports the Triple Aim framework, which strives to improve patient care, reduce the per capita cost of healthcare, and improve the overall population’s health. The EDW, which was updated within one week of source system changes in Level 5, is now updated within one day. Data content now covers external devices such as bedside devices and home monitoring data.

Level 7: Clinical Risk Intervention & Predictive Analytics

“Organizational processes for intervention”

Vendors at this level become fully integrated and dependent on predictive analytics, an influential tool utilized in health/financial care that estimates the likelihood of potential events occurring. One use case of it is to predict patient flow trends. Care sites such as urgent care centers that run without a set schedule especially rely on predictive analytics for patient satisfaction, reducing wait times, and providing an adequate staffing level. Visualization tools model these peak utilization times, which centers use to adjust and draft working schedules. Overall, analytics at this level are focused on “collaboration with clinician payer partners to manage episodes of care.” Registries are now flagged if a patient exhibits a mental or physical disability. The data content has further expanded to long-term facility data and patient outcomes. The EDW, going from one week to one day, is now updated in less than an hour of source system changes.

Level 8: Personalized Medicine & Prescriptive Analytics

“ Tailoring patient care based on population outcomes and genetic data”

The data-motivated culture at Level 8 finally expands to accommodate patient tailored care by using Natural Language Processing (NLP), prescriptive analytics, and interventional decision support. By calculating patients’ actions such as their risk factors for re-admissions and identifying patients’ who may be a no-show to their clinical appointment, centers can avoid financial ramifications and instead push for accessible, speedy medical care. In reducing individual patient costs, prescriptive analytics is used — a branch related to predictive analytics, but instead of focusing on data monitoring, it presents several data-based decisions to ensure the best course of action. After using predictive analytics to predict what is most likely to happen, prescriptive analytics evaluates the cost-effectiveness of treatments and simulates which procedure will provide the most optimal outcome. The data content also includes 7X24 biometrics, genomic, and genetic data. The EDW can now be updated within a few minutes of source system changes.

Final Thoughts

We have finally gone through all nine levels of the Healthcare Analytics Adoption Model. In experiencing the pressure from this unprecedented pandemic, a health enterprise must have a strong analytical focus to provide the best possible care — and Health Catalyst’s services are a benefit to vendors who struggle to reach this standard.

An undisclosed health organization recognized that their EMR (Electronic Medical Record) was unable to integrate data from different sources and failed to provide an accurate system-wide response to COVID-19. With the help of Health Catalyst, they developed a COVID-19 dashboard, which integrates more than sixty critical metrics (well- defined performance measures) within seven days, ensures speedy and improved quality of organizational response, and efficiently manages data from five hospitals and clinics in forty-five different locations.

A second example of a customer success story is another undisclosed health system with the same issue, having trouble integrating different data while issuing a poor response to the pandemic. Using Health Catalyst to visualize surveillance and testing data to monitor the outbreak, this healthcare organization can now effectively respond to individual COVID-19 cases, and can develop COVID-19 registries within fifteen minutes.