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The Truven Health Blog


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


Evidence That Telecommuting May Improve Employee Health


By Rachel Mosher Henke/Friday, December 16, 2016

 More people than ever work from home one or more days a week. The practice of telecommuting has taken the business world by storm.  Improvements in technology, the demand for more flexible work schedules and cost reduction strategies have contributed to this trend which has seen a dramatic increase over the last several years. Even people who live close to their place of work may take advantage of working remotely to eliminate commute time and provide flexibility to take care of midday appointments or family needs.  

Most of the attention on telecommuting has focused on how it impacts work productivity and opportunities for promotion.  But an important factor has been largely overlooked and absent from consideration – employee health.  Employers and employees can both benefit from learning how telecommuting affects health. 

Prudential Financial, a company with a long history in promoting work flexibility, in partnership with Truven Health endeavored to fill this gap and understand what affects telecommuting has on their overall employee health.  The research study looked at amount of time telecommuting and potential health risks including depression, stress, poor nutrition, physical inactivity, tobacco use, alcohol abuse, and obesity.

Studying a time period of two years, our research suggests that telecommuters had lower risk for obesity, alcohol abuse, physical inactivity, and tobacco use.  We found evidence that employees who engage in a small amount of telecommuting hours are likely to benefit positively from the activity including reducing their risk for depression.

The connection we found between telecommuting and lower health risks further strengthens the business case for support of flexibility and the connection between work-life and health.  However, it is important to note that while our study provides some evidence to suggest that flexibility has health benefits, maintaining some level of in-office work may help to strengthen spiritual and social health. In the case of 100% remote workers, managers may want to ensure extra effort is made to stay connected to those workers and create inclusive opportunities with the rest of the team.

The study timeframe was only two years, so more research is needed to understand the longer term impact of working from home on health. The results from our study of the Prudential program are specific to their employee programs.  And though not generalizable employers and health plans may be curious to see whether these health benefits translate to health care savings for their organizations. 

Truven Health Analytics is encouraged and eager to help organizations examine the relationship between telecommuting intensity and health outcomes. 

You can read more about our findings by downloading the full research brief, The Effects of Telecommuting Intensity on Employee Health.

Rachel Henke
Senior Director Behavioral Health and Quality Research

 


Risk of Hospitalization


By Therese Gorski/Tuesday, December 13, 2016

As Anne Fischer wrote in her last blog How a Data Scientist Thinks about Risk Stratification, in order to predict risk, we need to first determine what “risk” is being measured. One important risk is that of being hospitalized.  Risk of hospitalization or admission models have become more targeted, more personal and seemingly more prevalent. Further, they are very much in line with the goals of population health and the “quadruple aim” (improving patient care, reducing costs, improving the health of populations and improving the provider’s experience). If a person has proper ambulatory or outpatient care for a chronic disease, acute inpatient admissions related to that disease should be rare.

At Truven Health, we have been developing and maintaining risk models for decades. Many risk models, such as risk of cost, risk of mortality and risk of complications, are best used in aggregate within a population subset. This evaluation is typically done at a service line or patient group level, for example, all cardiovascular patients or all patients with a specific MS-DRG.

In today’s world of increasing interest in care management and targeted outreach, individual level risk models hold much promise.  These models evaluate and interpret risk for each individual patient. Several years ago, we started a journey to develop new targeted risk models – including risk of hospitalization – to meet this increasing business need. We focused on specific diseases with the intent to have high predictive accuracy overall but particularly at the “tails”, meaning we must perform well at identifying those who have a high likelihood of being admitted in the near future. Or, in data science terms, model performance was measured by its sensitivity, specificity and positive predictive value for all patients above a specific risk threshold, with an emphasis on high sensitivity (that is, reducing false negatives).

To start, we chose to focus on diabetes, congestive heart failure (CHF), asthma, and more recently chronic obstructive pulmonary disease (COPD). These conditions run the gamut for both prevalence and admission risk, with asthma being most prevalent and least likely to result in admission versus CHF, which is least common but most likely to result in admission. These are also chronic conditions that are typically managed, at some level, which also fit our criteria.

In addition to specific diseases, we also focused on identifying risk at one-, three- and six-month intervals so that a care manager can better understand the risk at hand and be able to prioritize accordingly. Further, we report risk across several categories including “all-cause admissions”, “potentially avoidable admissions” (largely defined by AHRQ) and risk of “related admissions” which represents conditions that are considered to be related to the main condition as defined by Truven Health. Finally, along with each model’s risk score, we provide the patient attributes that are driving the risk score, whether it be a recent hospitalization, level of disease severity or even age. We believe that this additional insight gives the care manager a bit more background on the patient, helping to explain why the patient may have an increased risk value.

These models have been notably successful in terms of their predictive accuracy and our work will continue as we expand the number of diseases and work with our clients to help make the information actionable. The general trend toward person specific risk versus risk in aggregate will only grow and will become more refined as we, and the industry in general, are able to obtain and incorporate more personalized information about people.

Therese Gorski
Senior Director, Advanced Analytics


Truven Health Risk Model Ranks High in Actuarial Evaluation


By John Azzolini/Monday, November 28, 2016

 

Healthcare payers today are facing the complexities of reform, increased competition, and budget constraints — all while dealing with pressures to reduce costs and improve member health. Managing health risk has become a necessity. But to manage risk, payers must first understand their population. To do this well, they need reliable, robust risk and cost of care models.

 

Last month, the Society of Actuaries (SOA) released a study showing that Truven Health Analytics’ cost of care model outperformed other risk models in 18 out of 22 measures. SOA’s Accuracy of Claims-Based Risk Scoring Models compared health risk-scoring models, building on their previous studies with similar objectives (the most recent was in 2007). In the medical claims category (predictions based only on medical claims data), the current study showed that, in 21 of the 22 measures, the Truven Health model was ranked either first or second. No other model came close to matching this performance. (See Table 1 for a summary of how Truven Health’s model ranked relative to the competition).

 

How the SOA Evaluates Risk Models

The SOA evaluated Truven Health Analytics’ cost of care model against six others:

 

  • ACG® System
  • Chronic Illness & Disability Payment System and MedicaidRx
  • DxCG Intelligence
  • HHS-HCC
  • Milliman Advanced Risk Adjusters
  • Wakely Risk Assessment Model

 

The SOA assessed all models on their ability to predict costs using the Truven Health Marketscan® commercial claims dataset of 1 million members, and used three methodologies to evaluate their precision: R-Squared, the mean absolute error statistics, and predictive ratios. All three methodologies measure the statistical difference between the prediction and the actual results. All models produced both a concurrent and prospective cost prediction and were evaluated using both a capped data set (where patient costs were capped at $250,000) and a non-capped data set.

 

The SOA evaluated the models’ predictive ability using a number of scenarios (total medical costs, simulated random groups, condition-specific predictions, patient cost). In the simulated random group scenario, the SOA created groups of 1,000 and 10,000 patients to simulate the application of the model to subgroups of the population.

 

Table 1: How the Truven Health Cost of Care Model Performed

The Truven Health model ranked first or second for its ability to predict costs in 21 of the 22 measures studied.

 

Scenario

Truven Health Model Ranking*

R-Squared

Mean Absolute Error

Non-Capped

Capped**

Non-Capped

Capped**

Total Medical Costs, Concurrent

2

1

2

1

Total Medical Costs, Prospective

1

1

1

1

Simulated Random Groups, Concurrent

2

3

1

1

Simulated Random Groups, Prospective

1

1

1

1

 

 

Predictive Ratios

 

 

Overall Condition Specific Prediction, Concurrent

 

1

 

 

Overall Condition Specific Prediction, Prospective

 

1

 

 

Very Low Cost Patients, Concurrent

 

1

 

 

Very Low Cost Patients, Prospective

 

1

 

 

Very High Cost Patients, Concurrent

 

1

 

 

Very High Cost Patients, Prospective

 

1

 

 

     * Compared with six other models.

** Capped at $250,000

 

Why Risk Models Are Important to Payers

Risk modeling is a very helpful tool for health plans and employers. It can provide valuable insights into member utilization patterns and risk– vital for benefit planning, disease management and wellness program management, and member communications. It can provide deep insights into provider performance, and aid in determining ideal reimbursement and premium rates. Such models are an integral part of a number of Truven Health databases and analytical tools. The SOA evaluation speaks to the high quality and reliability of the Truven Health solutions.

John Azzolini
Senior Consulting Scientist

EMPAQ for 2015

Full results are now available


By Truven Staff/Monday, November 7, 2016

 

The EMPAQ data collected for 2015 included information for four distinct categories, including incidental absence, and six key health and productivity programs. View the full results here.

 


How a Data Scientist Thinks about Risk Stratification


By Anne Fischer/Tuesday, October 25, 2016

“Risk”. It’s a word we hear every day in the healthcare industry. We want to avoid risk, we want to predict risk, we want to find patients that are high risk. We want to risk stratify populations (organize people into a set number of mutually exclusive tiers of increasing risk).

My recent blog posts have centered around the concept of Population Health. Clearly the idea of risk is particularly important in this world, where the goals are to keep well individuals healthy, avoid poor outcomes for those that are already sick, and minimize costs. Understanding, assessing, and predicting risk are all essential to this effort.

But what is “risk”? If you asked a physician, an insurer, and an average Joe on the street to describe “high risk” from a healthcare perspective, you would likely get very different answers. A physician might describe someone with high risk of developing a disease, high risk of a serious disease complication, or high risk of mortality. An insurer might describe someone at risk for a high amount of spending in the immediate future. The average Joe might describe someone at high risk for impairment/inability to function in daily life. Understanding the context-appropriate definition of risk is the first step toward building analytics to support risk analysis. And the appropriate definition is always dependent on the real world application.

Even when the application is understood, there is still considerable work to be done to identify the appropriate data and characteristics that lead to poor outcomes. Consider a discharge nurse who sees hundreds of patients a month as they prepare to depart from the hospital. Most knowledgeable hospital staff are aware that the most experienced discharge nurses will be able to tell you, with a high degree of accuracy, who is likely to show up back in the hospital in the near future. Multiple studies have tried to quantify the drivers of this type of “nurse’s intuition”. How do they know?

In 1964, United States Supreme Court Justice Potter Stewart used the now infamous phrase: “I know it when I see it” to describe his threshold test for obscenity in the case of Jacobellis v. Ohio. A discharge nurse might say much the same thing when asked to describe a patient at high risk for readmission. I know it when I see it. Characteristics such as illness burden, past behavior, social situation, self-care ability, home support, and others are often referred to, but the reality is that it’s the entire picture, and often a bit of an ambiguous “gut feeling” thrown in for good measure.

So how does Data Science fit into this picture? Our challenge as Data Scientists is to turn “I know it when I see it” into a measurable mathematical formula, so that everyone “knows it” even without seeing it in person. It involves extensive experimentation with different data sources, variables, and modeling techniques, as well as building in the capability for models to evolve and learn over time. At Truven Health Analytics, my team is exclusively focused on developing and testing new models, using various kinds of data that are readily available to us. In future blogs, we’ll describe some of these models including risk of developing diabetes and risk of admission. Truven Health, an IBM Company, now is positioned to move deeply into this space and develop these types of risk models by bringing together traditionally disparate data sources, clinical knowledge, and cutting edge modeling techniques.

Anne Fischer
Senior Director, Advanced Analytics


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