In this special feature story, three analytics experts offer healthcare CIOs and other IT leaders some best practices for getting analytics to work.
Analytics technology has proven itself immensely valuable to healthcare provider organizations. It helps both clinical and administrative leaders throughout an organization improve care, trim costs and gain efficiencies.
But to get the biggest bang for the buck, CIOs and other leaders throughout the healthcare enterprise must optimize analytics to work best for their organization. Analytics is not a simple plug-and-play offering. Leaders must fine-tune the technology and the approach to the technology so it works to best fit their organization’s needs.
Here, three healthcare analytics experts offer best practices when it comes to optimizing analytics technology to best meet the needs of an individual healthcare provider organization.
Understanding the business case
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To best optimize analytics technology, healthcare CIOs must have a solid understanding of the business use-case as well as how the technology should be used to meet today’s needs and future requirements, advised Sita Kapoor, CIO at HealthEC, a 3D analytics technology vendor.
“When optimizing for business use-cases, look to see how mature the business uses are,” she explained. “Will they be able to use new-generation tools, or do they need static reports to do job functionalities? What type of data are they looking for? Is it a more cost-driven, utilization-driven or quality-driven process, or are they very mature business users looking at more machine learning and deep neural networks to do risk stratification?”
“What do they have today and where do they need to get in order to meet the market’s future needs?” she said. “With the cloud becoming more and more of a stack that people are moving toward, CIOs must figure out a strategy to get there with their current technology and the technical team that’s already in place. If necessary, they can move toward a hybrid mode or outsource to a vendor that can help make their technology stack more in line with today’s marketplace.”
Analytics focused on the patient
The best hospital analytics approaches tend to be centered on the patient: A patient is something every consumer will become at some point in their journey – some by surprise, such as an accident, others with more chronic conditions, which require steady-state treatment, said Cameron Thompson, managing director, healthcare, at Acxiom, an analytics vendor.
“The patient expects more now than ever, including personalized treatments and care teams that have the up-to-date details about their condition and latest approaches,” he stated. “Using analytics as a provider to better understand your patients is of utmost importance. Doing so begins with taking known data within the provider organization, combining it with outside information, and building a portrait of the patient that supplements the care team goals.”
This approach is used successfully to create better customer experiences in other consumer-facing industries but has been widely misunderstood in healthcare, Thompson cautioned.
“The key distinction is that in healthcare, you’re creating a portrait not just for consumer preferences, like a car manufacturer may, but rather derived analytics insight for the narrow use of predicting future outcomes,” he explained. “Things like the likelihood to participate in clinical trials or identifying high-risk patients prior to occurrence. It is in these narrow use-cases for viewing patients where added, third-party data can provide the insights needed to be predictive.”
The hospital setting can analyze and establish a baseline to predict oncoming occurrence or willingness to engage in a particular treatment, he added. Understanding the patient through predictive analytics using outside data is one of the most effective and efficient methods of keeping the patient at the center of the hospital’s patient care approach, he contended.
“As care plans within critical care versus hospital networks vary, based on resources and goals, understanding and segmenting patients is a useful method to begin personalizing treatments and predicting the healthcare needs of a community,” he said. “After all, consumers demand more personalized and connected care than any time in history. This approach is one effective way to meet consumer demand while not driving unneeded expense within the hospital.”
The need for insights and silo-busting
Astute and visionary healthcare leaders have realized that as their organizations have become more complex (centralized, globalized and digitized), they have also become data-rich but insight-poor and siloed, contended Sean Price, EMEA director, industry solutions, public sector and healthcare, at Qlik Technologies, an analytics technology vendor.
“There now are so many data sources being captured and stored in multiple locations – a blizzard of data swirling around with untapped value,” he said. “Data, which is one of the most valuable assets in healthcare, is many times being left undiscovered and underutilized. It is not uncommon for healthcare trusts to have hundreds of systems operating that carry huge latent value.”
One best practice for optimizing analytics is a mindset change to treating data as a critical strategic imperative to shine a light on every part of the business, he advised.
“Innovative CIOs will be able to leverage analytics across the entire organization to democratize and industrialize every possible set of data, from the board to the front-line,” he said. “Doing so will create a line-of-sight or golden thread that surfaces the areas for improvement across people, process and system.”
This approach puts CIOs in an enablement role through analytics – empowering people closest to decision and action with relevant data, Price said. The shift brings about a cultural change in assurance, productivity, quality and overall outcomes, and ultimately the culture for delivery, he said. This is not about producing dashboards with KPIs – this is about providing actionable intelligence to drive business, he added.
“A good example of this is the analytically powered command center,” he suggested. “This approach brings patient flow into a near-real-time environment where patient safety, experience and cost can be efficiently managed through the end-to-end process. Demand, resource and capability can be both strategically and tactically managed, and when connecting this approach to process improvement techniques, it can yield significantly improved outcomes and notable efficiency savings.”
Peering into operational costs
Thompson of Acxiom offers another best practice when optimizing analytics, noting that healthcare analytics has been in high demand to keep provider operational costs down.
“One such example was a hospital looking to build a new treatment center within the high cash-pay sector,” he said. “They analyzed the demographic and psychographic regional data to predict how many centers were needed, and even where they should be located. These centers often take years to recoup value, and this type of operational analytics can drive substantive savings for the operator.”
In conjunction with this approach, workforce mapping is another way to keep operational costs down, matching the potential workforce with the patient population to align the resources needed after the future buildout, Thompson said.
“These analytics use-cases support the future-proofing of new capital expenses around vibrant hubs of future employment,” he said. “Finally, operational analytics helps predict within the hospital where useful diagnostic equipment can be utilized as well as demand for such services within the region.”
The role of analytics in these settings has long delivered good planning results and operational efficiencies, he added. Cohesive capital planning and coordination between the patients’ future needs are powerful developments in healthcare analytics, he said.
The business intelligence curve
On another optimization front, once the CIO has built the analytics platform and connected all the relevant data, he or she then should start to think about moving up the business intelligence curve, said Price of Qlik Technologies.
“The business intelligence curve shows the relationship between how more advanced analytical techniques like predictive analytics yield a greater competitive advantage,” he explained. “Innovative CIOs are moving insight creation from raw data through descriptive, diagnostic, predictive to finally a prescriptive approach in a rapid roadmap process.”
CIOs need to embrace the reality that traditional waterfall approaches don’t cut it: They must drive an agile approach that delivers value back to the business in weeks, not years, he added.
“A good example of getting up the business intelligence curve can be seen in applying predictive analytics to areas such as length of stay; readmission risk; hospital falls; stroke risk; and predicted demand and resource requirements for any hour of the day,” Price suggested.
“While moving up the business intelligence curve, CIOs must also remember to deploy data literacy training for the broader organization,” he continued. “Traditionally, analytics has been pointed toward the privileged few, the preserve of the analyst or manager. As CIOs bring analytics to the front line, at the point of delivery, the greater benefits – both hard and soft – can be lost if the newly data-enriched staff is not equipped with the skills needed to understand and leverage data effectively.”
Source: Healthcare IT News