Healthcare Analytics Software Solutions: A Definitive …

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The use of healthcare analytics software is at an all-time high at health systems across the United States. In fact, an eHealth Initiative survey asked 102 healthcare organizations about their use of data and analytics and discovered that a whopping 90 percent use analytics for their quality improvement initiatives and revenue cycle management. In that same questionnaire, 82 percent identified the importance of using analytics for population health management even though some haven’t started to use analytics for this purpose yet.

Why the sudden analytics trend? The reality is thatanalytics can unlock the tremendous insight of stored hospital data and help organizations become data driven. The knowledge can then be used to drive systemwide improvements for clinical and operational benefits. In fact, a 2015 report from CDC states that because of analytics, organizations are already realizing gains like these: 82 percent improvement in patient care, 63 percent reduced readmissions rates, 62 percent improvement in overall health outcomes, and 54 percent improved financial reporting capabilities.

Cut Through the Marketing Hype

Because of the high demand for analytics, there are many players in the market. Each player is touting its ability to provide much-needed software solutions. Few, however, have the advanced technology that will enable health systems to overcome current data challenges, such as the siloed nature of the data; the many different source systems data is stored in (e.g., finance, research, administrative); and the constantly changing business rules of healthcare.

With so many different solutions available now, health systems need to be able to cut through the marketing hype to find the tools most fitting for their needs. And while choosing the right analytics solution is of vital importance, it’s just as critical to adopt a foundation that enables the software to harness the full power of the stored data.

The Foundation for All-Things-Analytics: An EDW

healthcare enterprise data warehouse (EDW) is the most important first step on the journey to becoming data-driven. EDWs pull in and organize all of a health system’s siloed data (e.g., clinical, financial, patient satisfaction) into a single source of truth. It’s here, in the EDW, that users can analyze data for near real-time answers to drive improvement initiatives.
Once the EDW is in place, its unique ability to provide a single source of truth enables users without technical skills to interact with the data. Also, because of the data integration that occurs within the EDW, users can dig into previously siloed data and realize more ROI from existing source systems than ever before. For example, Texas Children’s Hospital used an EDW to create near real-time reports from data from across the organization. Now, on average, each EDW report costs 67 percent less to build than an EHR report. The savings are significant; especially considering the hospital was producing about 1,300 EHR reports per year at an average cost of $4,832 per report. In addition, each EDW visualization delivers, on average, 10 times the value of EHR-generated reports. Reports that once took weeks or even months to prepare can now be generated in days, and sometimes hours.

Sample EDW reportA sample EDW report

Analytics Software to Integrate Data from the EDW

Once the EDW is in place, the organization has the necessary foundation to begin to adopt sophisticated analytics software. These solutions integrate on top of the EDW and enable users to pull the relevant data to answer their questions.

Because different types of analytics solutions are designed to solve different needs, it can be difficult to know which solution to choose. The following three types of solutions are designed to help health systems get the most out of their EDW and existing source systems:

  1. Foundational Reporting Analytics Applications

Foundational reporting applications automate the process of providing users with access to data through an efficient reporting and data distribution system. In addition, the applications display dashboards and basic registries for a broad range of clinical and operational conditions. They also show critical information about trends and patterns in the data.

How one health system used a foundational application to improve clinical inefficiencies

Texas Children’s Hospital had an electronic health record (EHR), but it wasn’t meeting clinicians’ needs for providing useful data. What data physicians did get, was through a cumbersome, time-consuming process. To solve this problem, first, Texas Children’s implemented an EDW. Then it adopted a foundational application, Population Explorer. With the application’s registries and library of commonly defined measures, the hospital’s improvement teams could identify and choose short-term and future projects quickly. Texas Children’s has been able to reduce the time required to develop clinical program improvement projects by 85 percent.

Sample population explorer visualizationSample Population Explorer visualization

  1. Discovery Analytics Applications

Once the foundational reporting structure is in place, the organization can advance to discovery applications and mine the EDW’s data. Discovery applications give users the ability to discover detailed patterns and trends in the data. Users can also drill down into the data to pinpoint which areas to prioritize for quality and cost improvement initiatives, including the ability to:

How Crystal Run Healthcare used discovery applications to improve operational and clinical inefficiencies

Crystal Run Healthcare’s leaders wanted to ensure financial stability as the organization started to take on more risk and growth as an Accountable Care Organization (ACO). In specific, they wanted an analytics solution that would enable their teams to continually make improvements in areas like clinical variation. The leaders also wanted to address day-to-day operational challenges, such as growth and expansion, risk-based contracting, physician compensation, and population health management. To addresses these issues, Crystal Run adopted a late-binding data warehouse. Then it implemented foundational applications, such as Cohort Builder and Comorbidity Analyzer. The organization is now able to answer clinical and operational questions up to 98 percent faster than using legacy analytics solutions. In addition, it is able to negotiate better risk-based contracts with payers. It is also able to prioritize areas that need improvements.

Sample comorbidity analyzer demographic tab Sample Comorbidity Analyzer demographics tab

  1. Advanced Analytics Applications

Advanced clinical analytics solutions are the key to solving healthcare’s most pressing challenges, such as population health management, clinical risk intervention, predictive analytics, and ultimately, personalized medicine. They are powerful tools enabling users to target a specific clinical process (e.g., disease condition or procedure) or an operational support service (e.g., operating room workflow). Once the process or service has been identified, users can then track, measure, and sustain the improvements by making adjustments based on the effectiveness of their interventions.

How a healthcare system used advanced analytics to improve follow-up appointments for heart failure patients

A not-for-profit healthcare system needed to deliver high-quality data to their heart failure clinicians to reduce readmissions.

After implementing an EDW, the organization put an advanced analytics application in place—Population Analytics Advanced Application Heart Failure Module. The application enabled users to establish readmissions baselines, track performance metrics, and distribute information to everyone involved in the initiative. With the support of Health Catalyst’s experts in clinical implementation services, the team established a comprehensive heart failure analytics platform in just 90 days. The team now has baselines for 30- and 90-day readmission rates. They also have an analytics platform that enables the team to evaluate the impact of their quality interventions on readmission rates in near real-time. They’ve also experience a 270 percent increase in documented follow-up appointments because they can now track appointments.

Considerations to Keep Top of Mind When Choosing an Analytics Software Vendor

Choosing the right EDW and analytics software solutions are critical first steps to becoming data driven. But there are a few other considerations to keep in mind, especially when choosing the vendor. Considering the following points will guarantee the best ROI from any analytics investment.

Cultural Change Readiness

Changing an organization’s culture to become data driven can be a challenge. But this change is necessary for organizations to realize the full value of an analytics investment. There is, however, a way to successfully bring about this shift in culture by gaining leadership’s (both clinical and administrative) buy-in and support. The right vendor will assist organizations with this process.

Total Cost of Ownership

The best analytics software solution in the world is of no value if it’s not affordable, which is why it’s important to measure the total cost of ownership (TCO). To do this, add up the three-year labor costs, licensing fees (including third-party), support fees, and hardware costs associated with the solution in mind. The TCO over three years should be evenly distributed, not front-end loaded. The contract should be structured with escape clauses if the vendor’s solution cannot prove value in the first year. In today’s market, health systems should expect initial value from their software in less than six months, and preferably only three months. If a vendor cannot or will not commit to this timeframe, look for another vendor.

Company Viability

Choosing an analytics software solution shouldn’t be a one-time investment. It’s best to choose a vendor with vast industry knowledge. The vendor should also be there to help the health system implement the EDW and then continue as a partner during the various stages of the implementations. This approach ensures the health system will gain the best ROI from their investment.

To determine a vendor’s viability the following questions must also be answered:

  • Longevity: Will the vendor be around in nine years (the average life span of a significant IT investment)? If not, it is possible to continue on without them?
  • Financial strength: Is the vendor in solid financial shape? What’s their monthly burn rate vs. income? How many days’ cash-on-hand do they maintain? What does their sales pipeline look like?
  • Leadership: Does the vendor’s executive leadership team have a track record for jumping from one company to another or do they have a track record of longevity and success?
  • Resource investment: How much is the vendor spending on sales staff in comparison to engineering and product development staff? The best products are supported by a very lean sales staff. That’s because great products sell themselves.

Financial and Clinical Success Requires Technology and the Right Partner

Finding ways to achieve both financial success and clinical success in today’s value-based care environment are top of mind for health systems. They especially want to do the right thing for their patients by providing better clinical care. Organizations also need to reduce costs. The answer is to become data driven by adopting technology solutions. But there’s a lot of hype in the marketplace about which solutions are the best, and not all analytics solutions are created equal. Not all vendors are up to the task either.

To get the best ROI from any analytics investment, it’s important to assess the vendor. They should offer an EDW and various levels of analytics software solutions that cover every possible analytics need a health system will have. They also need to be financially viable and be able to assist the organization with culture change. If vendors can’t commit to delivering value soon after the implementation of the solutions, find another vendor. Once health systems have the right partner and technology solutions, they will be well on their way to becoming data-driven organizations. But even more important, they’ll be able to solve their most pressing problems.

What analytics software solutions have you invested in? Have they given you the insights you need to become data driven? If not, what are they lacking?

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