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The Truven Health Blog


The latest healthcare topics from a trusted, proven, and unbiased source.


Faced with a health system or hospital budget shortfall?

Peer benchmarking could lead to the answer.


By Truven Staff/Wednesday, September 13, 2017

Tell us if this health system’s challenge sounds familiar: CHRISTUS Trinity Mother Frances Health System, located in Northeast Texas, was facing a staggering potential setback when a number of payer contracts changed. The difference amounted to a $25 million shortfall in their budget’s revenue.

The system’s first reaction might have been to issue an across-the-board expense reduction mandate to make up the budget difference. We all know that can happen a lot in the industry, but it doesn’t always produce the results healthcare organizations need, and quality of care can be impacted.

Instead, this system chose a data-driven, strategic savings approach as the path forward, with an eye on long-term financial independence from these types of shortfalls.

A look at the targeted expenses

Using a comprehensive comparative database, the system was able to benchmark costs, productivity and resource utilization against best-in-class facilities of similar size and demographics.

Leaders identified cost improvement opportunities in areas such as supply, labor costs, length of stay and purchased services — areas where the system was not at the same level as high-performing peers in terms of expenditures.

The benchmarking information from the database was also used as a call to action for staff to find methods of improving processes and cost management. CHRISTUS Trinity Mother Frances leaders formed teams and assigned financial targets. Teams then used the database to answer the question, “If another health system is able to keep supply costs at this level, what can we do to bring our costs to that level with no bearing on our patient care or satisfaction?” The health system also created a dedicated project management office to help guide the process. The results of these efforts (in box below) speak for themselves.

If you’d like more information on how the health system achieved this result, please reach out to usYou can also read the full case study here.

 

 


Barriers to Adoption of Clinical Data Analytics for Population Health


By Larry Yuhasz/Saturday, August 3, 2013
Larry Yuhasz imageIn the recent article, Data Analytics Continues Upward Trend, the authors discuss the growth potential of healthcare analytics and the factors that will enable or inhibit this growth. Although the clinical data analytics trend is casting upward, momentum is being held back by a few key factors. First of all, the lack of electronic medical record (EMR) interoperability tends to silo clinical data by care setting and facility. Many analytic requirements to develop predictive risk scores, prevent readmissions, and measure risk require the ability to analyze clinical data across care settings and facilities. The demands of payment reform will ultimately prevail, yet Integrating the Healthcare Enterprise (IHE) vendor protection of proprietary formats remains strong.

Second, the majority of the U.S. health system is operating in a fee-for-service business model. Not until the majority of revenue shifts to at-risk models will the requirements for population health analytics really blossom. This is happening in enlightened pockets across the country and requires leadership education and changing HIT investment strategies to take root.

Third, many hospital systems are not operationally experienced in implementing enterprise-wide decision support. Unlike health plans and carriers who have been leveraging information to manage their business for decades, hospitals have tended to manage their operations along siloed service lines, with their physician network ultimately calling the shots on resource requirements. Payment reform fundamentally changes this dynamic and sees many more physicians being employed by physicians and connected with analytical platforms that can guide not only point of care decision making, but also retrospective review of clinical and cost performance.

Finally, all healthcare data emanates from patient encounters. Claims data is triggered via coding work flow to optimize billing, whereas clinical data is captured based upon proprietary EMR data entry requirements.  In today’s fee-for-service world, the clinical coding leveraged for claims purposes may or may not jive with the clinical data fields entered for EMR collection purposes. This creates downstream data aggregation and analytical methodology challenges. Over time, as payment reform stimulates a higher percentage of value-based care, the collection of administrative and clinical data must not only become more efficient at the encounter level, but also more analytically relevant for real time and retrospective analytical purposes.

Ultimately, the pace of analytical growth will be enabled through a combination of payment reform, operational change across all healthcare stakeholder groups, and technical innovation that overcomes barriers to data flow and utility.

Read more about our comprehensive suite of solutions for improving care and managing population health.

Larry Yuhasz
Director for Strategy and Business Development

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