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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


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