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The Truven Health Blog


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


Three examples of how performance improvement opportunities were hiding in this health system’s data


By Truven Staff/Monday, October 16, 2017

Like most health systems, Indiana University Health System (IU Health) leaders knew they had large amounts of valuable data stored throughout the organization. The challenge for the largest health system in Indiana was how to use the often-segmented data to quickly identify opportunities for cost controls and financial decision making, especially at the regional level.

To meet this challenge, the system pulled together a results-driven group called the Profitability & Utilization Support Hub (PUSH), made up of experts in revenue cycle, supply chain, clinical operations, labor analytics and more. The team’s charter: Make data-driven recommendations that could eventually drive operational and financial improvements.

The PUSH group began to identify opportunities by leveraging a comprehensive comparative database. The robust and detailed benchmarks allowed the team to see where their organization stood compared to peers.

Here are just three examples of how the team incorporated performance benchmarking to fuel its recommendations:

#1  Reducing above-average telemetry usage

The group proactively found that telemetry usage at one of the system’s hospitals was 70 percent higher than the comparison group median. The PUSH team recommended a review of the hospital’s electronic medical record order set and staffing ratios, which led to changes. The hospital’s telemetry utilization was reduced to the comparison group median within three months.

#2  Increasing oncology patient volume

The health system had recently converted one of its hospitals into an outpatient facility offering emergency department (ED) and oncology services. Using comparison data, the team discovered an imbalance between oncology patient levels and staffing levels. Leadership acted quickly and launched a campaign to increase oncology patient and provider volumes to align with staffing levels.

#3  Boosting ED capacity

The chief financial officer of an IU Health hospital asked the PUSH group for guidance to figure out why ED visits were declining even though staff reported full capacity. The data showed the hospital was efficiently using its available space — treating 20 more ED patients per day than the comparison peer average — but it had six fewer treatment spaces. Based on that insight, the hospital received approval to expand to accommodate 25 more visits per day.

If you’d like more information on how the health system achieved these remarkable results, please reach out to us

 


Measuring the potential impact of a new palliative care service line


By Truven Staff/Monday, October 16, 2017

When you discover your mortality rates for stroke, pneumonia and heart failure aren’t where they need to be, what do you do?

The quality team at Carson Tahoe Health, a health system based in northern Nevada and eastern California, recently faced that challenge — and knew they had to answer two key questions: why is this happening and what can we do about it?

After a concentrated chart review, the health system discovered that the majority of patients who died in their care were at the end stages of their diseases.

Recent studies had pointed to the implementation of palliative care as a way to improve patient care and lower mortality rates. So to test the theory in their own environment, Carson Tahoe Health decided to roll out an inpatient care protocol in which hospitalists refer patients with end-of-life issues to a palliative care physician.

Comparing outcomes to determine progress

Using a clinical performance monitoring and benchmarking solution, the quality team was able to analyze several metrics, focusing on heart failure and chronic obstructive pulmonary disease (COPD) diagnosis-related groups in end stages of the diseases.

They tracked two groups of patients: those who participated in the palliative care protocol and those who did not. That way, cost, utilization, readmission, length of stay and other comparisons could be made.

The health system gained some key insights on the value of palliative care:

To see if they could move the dial even further on care improvement, the health system built and analyzed population reports to review the potential return on investment for an outpatient palliative program.

Informing the next step

With the new data and reports in hand, the health system proposed an outpatient palliative care service line to its board of directors, and the board approved it. Now Carson Tahoe Health offers a palliative care/heart failure chronic disease management clinic that sees patients within five days of discharge.

Seeing progress

After implementing the palliative care initiatives, the health system’s 30-day inpatient mortality rates for acute myocardial infarction, heart failure, COPD, pneumonia, stroke and coronary artery bypass graph surgery began trending lower than the national average.

They plan to expand palliative care services to other chronic end-stage disease groupings in the future.

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


The Next Chapter of Enterprise Analytics for Payers Part 3 - What is Your Enterprise Analytics Roadmap?


By Truven Staff/Thursday, October 5, 2017

This excerpt originally appeared as a post on the Watson Health blog. Read part two.

Payers can deploy advanced analytics on top of their EDWs to enable reliable, faster decision making across the organization, and to enhance the value analytics teams can deliver. But with numerous business intelligence needs and stakeholders, prioritizing can be challenging.

We recommend the following four-step approach:

Align Analytics Initiatives with Business Priorities

Begin by identifying the strategic initiatives and business activities that could be most positively impacted by enhanced analytic insights. Some questions to consider include:

  • What are the key business imperatives?
  • Where can the greatest impact be made with support from advanced analytics?
  • Which business functions and stakeholders
  • require more timely information?

Identify the Analytics Required to Meet the Business Needs

With specific business priorities in mind, the next step is to determine what analytics are needed and how they’ll be visualized. You’ll want to assess the current suite of analytic tools and capabilities employed to address stakeholder needs including:

  • Grouping methodologies
  • Risk and severity models
  • Clinical rules
  • Reference data

Determine the Timeliness of Reporting Needs

As advanced analytics are selected for the analytic environment, payers should also evaluate their fit with existing business intelligence tools and capabilities. A few questions analytics leaders can ask to get started include:

  • Are analytic data marts easily accessible to analytics teams?
  • How current will the data need to be for various business reporting subject areas?
  • Would a conformed dimensional warehouse model speed user analysis and allow root cause to be evaluated?

Monitor and Adjust

Implementing a comprehensive EDW and advanced analytics strategy will likely be an iterative undertaking. That’s why it’s important to view your EDW and analytics initiatives as an ongoing process that doesn’t need to be perfect to get started. Regardless of where you are in the EDW-analytics journey, you’ll want to designate time quarterly or bi-annually to revisit the analytics roadmap and evaluate whether needs have changed.

In today's complex healthcare landscape, the path to success for payers will likely require a greater reliance on enterprise data and the critical insights that can be derived from that data. Licensing proven analytics that can be layered on top of the EDW can help accelerate time to insight. Click here to learn more about our new solution, Flexible Analytics.


The Next Chapter of Enterprise Analytics for Payers Part 2 - Is your data ready for advanced analytics?


By Truven Staff/Thursday, October 5, 2017

This excerpt originally appeared as a post on the Watson Health blog. Read part one.

Healthcare Payers today can increasingly leverage proven, off-the-shelf methodologies in their own technology environments to help accelerate business insights and transformation.

Completeness Checks

An audit, balance and control (ABC) framework should be in place to identify and limit the impact of missing data, and you’ll want to determine acceptable thresholds when data is missing. Establishing these thresholds and benchmarks by field will focus the data warehouse team on areas that need further improvement, as well as allow users to contemplate the impact incomplete data could have on their analytics and reporting.

Validity Checks

It’s important to conduct validity checks on fields that should contain standard codes or elements, and compare recorded values to lists of possible valid values for that field. When these validity checks flag unexpected values, you can establish the validity of the nonconforming code. If new values have been added to the coding scheme, it might be necessary to update of the conversion program or code lists.

Reasonableness Checks

Consider conducting reasonableness checks to ensure the data makes sense. For example, look at the relationship between two or more related columns, or between a column and benchmark data, to confirm they are reasonable. Examples of reasonableness checks include ratio of surgical services to total services, percentage of non-specific diagnosis codes, and ranges of average cost per service by procedure code.

Click here to read part three of the blog series, which explores how payers can develop their enterprise analytics roadmap to help them prioritize and roll out analytic resources and initiatives across the business.


The Next Chapter of Enterprise Analytics for Payers Part 1: How Mature is your Enterprise Data Warehouse?


By Truven Staff/Thursday, October 5, 2017

This excerpt originally appeared as a post on the Watson Health blog. Read the intro.

In the introduction to this 3-part blog series, we discussed how a new approach to enterprise analytics can help payers accelerate business value and transformation by leveraging proven, off-the-shelf methodologies.

There is only one

A data warehouse is an EDW if it holds all your information and makes it available for various stakeholders across your company. If there are other data warehouses in use for reporting that have overlapping subject matter or additional information not in or derived from the EDW, then analytics and reports may be generated that differ or compete with one another.

Maturity goal: A single EDW that holds all the business information necessary to support team members.

Latency and freshness

The timeliness of analytic data should meet the needs of your business operations. If your users need a daily report of patients who are being admitted or discharged from the hospital, a weekly refresh of your EDW will not meet the business expectation. Not all situations require real-time data latency, but nearly all situations require consistent and dependable data refreshes.

Maturity goal: An EDW with data refreshed on a frequency that aligns with your business operations.

Data mart strategy

Various project teams will need different views of the same data to answer specific business questions. Though it's critical for the underlying data and definitions to be consistent, the questions that are asked by different stakeholder groups will vary. If your end users are building their own copies of your data to fit their needs, then your EDW maturity level may be low. 

Maturity goal: An EDW that allows stakeholder groups to customize views, rather than making copies of your data.

Usability and training

Users should be able to navigate the domains of your EDW easily after training and online guidance. If more than 80 percent of new query and report needs are completed by users without a call for assistance or a support ticket, your EDW is considered mature.

Maturity goal: An easy-to-use EDW that enables users with basic training.

Governance and growth

Another aspect of EDW maturity is its ability to handle change and adapt accordingly. If project teams feel like it is too much work to create new domains or implement changes, your EDW strategy and solution may be at risk.

Maturity goal: An EDW that can scale as your business needs evolve.

Click here to read part two of the blog series, which explores three types of data quality checks payers can implement to advance their data management practices and analytics initiatives.


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