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The Truven Health Blog


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


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.


The Next Chapter of Enterprise Analytics for Healthcare Payers


By Truven Staff/Thursday, October 5, 2017

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

Healthcare payers today are facing a changing healthcare marketplace that demands business model transformation and the redesign of operational processes. In this new paradigm, information-centric strategies that support data-driven decision-making across the enterprise can help payers compete.

In many cases, payers have the data they need, but they might struggle to analyze it. Existing analytics strategies that may be falling short include:

    Building and maintaining analytics in-house, which can consume valuable time and resources.

    Deploying a best-of-breed approach, which can require health plans to piece together disparate methodologies licensed from multiple vendors, often leading to disconnected data and analytic context.

    Outsourcing data analytics, which can limit payers' ability to choose which analytics to run and when.

To help drive business transformation, payer technology and analytics leaders can increasingly leverage proven analytics in their own technology environments. This is an approach that can not only accelerate time to value, but can also help meet the reporting needs and requirements of business operations.

Click here to read part one of this series, which explores the measurable characteristics payer technology leaders can use to assess their EDW maturity and identify opportunities for improvement.


Quantifying the Impact of Stress on Your Employee Population's Health


By Emily Gugger/Wednesday, June 14, 2017

Research has consistently shown a link between stress and employee health. The Centers for Disease Control and Prevention has documented studies over the past 20 years that demonstrate a connection between the role of stress and the development of not-as-visible impacts, such as cardiovascular disease, musculoskeletal disorders, psychological disorders, and others.1

We examined the impact of self-reported stress on healthcare cost and utilization, as well as the prevalence of chronic diseases. As employers are increasingly offering programs targeted at managing stress, we wanted to evaluate the relationship between stress and employees' claims-based healthcare experience using our MarketScan® normative database, which contains the healthcare experience of more than 120 million privately insured individuals and spans 18 years.

A total of 238,498 active employees met the study criteria, which required them to have self-reported data on stress and continuously be enrolled with medical and prescription drug coverage. Using their health risk assessment data, we grouped these employees into three separate levels: little or no stress (58% of the group), stressed, but coping (27%), and stressed, not coping (15%).

Results from the Study

Our analysts adjusted the results using linear regression models that controlled for age, gender, geographic region, plan type and whether employees were paid on an hourly or salaried basis. The study found that:

  • Females younger than 40 were more likely to be stressed.
  • Employees who identified themselves as stressed, not coping were 200% more likely to be diagnosed with depression than those who identified as having little or no stress.
  • Those who identified themselves as stressed, but coping had a 15% higher claims cost after application of contractual discounts (annual allowed amount per member per year) than those who reporting having little to no stress.
  • Employees who identified as stressed, not coping had 53% more emergency room visits.
  • Coronary artery disease was 64% more prevalent in employees who identified themselves as stressed, not coping than those who reported having little to no stress.

What Can Employers Do With These Results?

Employers can analyze the impact of stress in their population and use the results to inform strategies to build a culture that allows employees to be more resilient in handling stress. Contact us to find out how we can help with disease management and other program evaluation.

Emily Gugger, Analytic Advisor
Payer Analytics & Consulting

1 “STRESS ... At Work,” National Institute for Occupational Safety and Health Education and Information Division, CDC, Publication Number 99-101, updated June 6, 2014, https://www.cdc.gov/niosh/docs/99-101/pdfs/99-101.pdf

 


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