How Big Data Can Improve Home Healthcare

by   |   August 26, 2015 5:30 am   |   3 Comments

Billions of bytes of data are created every day. In fact, more data is created in a single day in 2015 than was created in the entire year of 2003. And that data is being applied to glean insights and improve processes and outcomes in nearly all areas of life.

Healthcare providers are using the data collected from millions of patients to develop new best practices for care and to find ways to make healthcare delivery more efficient and effective. Hospitals’ use of big data for treatment and research is well documented. But the home healthcare segment also has the potential to see major benefits from big data.

National Association for Home Care and Hospice (NAHC) data show that more than 12 million Americans use home care services for acute illnesses, long-term care for chronic illnesses, and for care related to permanent disabilities. According to the NAHC, home care has been proven to reduce costs related to hospital readmissions, reduce the length of stay, and improve outcomes through improved support from family and friends as well as heath care providers.

Much of the evidence for these claims comes from the analysis of reams of data collected by home healthcare providers since the 1960s, when home health began to gain momentum as a viable aspect of the healthcare continuum. However, recent advances in data collection and analysis allow providers to better identify patterns, predict patient behavior and outcomes, more effectively monitor patients for potential issues, and prevent adverse events – all among the cornerstones of effective home healthcare services.

Pattern Identification and Predictions

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Pattern identification can be a vital part of disease prevention and treatment. For example, the CDC uses the data collected from more than 700,000 doctors nationwide in its flu tracking system to help public health officials stay on top of flu outbreaks, determine which strains need to be included in annual flu vaccines, and when those vaccines need to be changed. The organization has been collecting flu data since 1976, but the amount of data collected has skyrocketed since 2009, when the outbreak of H1N1, commonly known as “swine flu,” significantly increased the amount of data being reported to the CDC.

Now, thanks to data submitted by labs, doctors’ offices, and hospitals from all over the country, the CDC and sites like Google Flu Trends can offer up-to-the-minute information about the location and severity of outbreaks, the number of hospitalizations, and treatment protocols. Home health agencies put this data to use by increasing employee education and protection protocols, and by disseminating the most recent flu information to their patients, thereby helping slow the spread of disease and keeping vulnerable patients out of the hospital.

This type of pattern analysis extends well beyond flu containment and prevention. On an individual level, home health providers can look at the data collected in healthcare software on groups of patients to identify patterns and use that information to solve problems and improve care for individuals. Boston Children’s Hospital, for example, is involved in research that uses data collected from across departments to more effectively manage medication and prevent adverse events. By looking for patterns in detailed data relating to adverse events and medication orders, analysts are better equipped to address previously unknown risks and develop better interventions. On the home healthcare front, providers may look at the data surrounding missed medication to identify commonalities among patients who miss doses and develop new policies and practices to prevent patients from missing doses in the future.


Dr. Jeffrey Brenner, of Camden, NJ, is one provider who thinks that big data in the healthcare environment tends to be too focused on prediction. He believes that, in addition to identifying patterns to prevent future issues, the data being collected can be used to better analyze patients’ current problems and address them more effectively. His organization, Camden Coalition of Healthcare Providers, uses data from a variety of sources, including ambulance calls, patient records, and conversations with doctors, to identify the patients who are “super users” of hospital services and find interventions that will keep them out of the emergency room. The interventions include the services of home health providers.

On an individual level, providers use the data collected on a large scale to identify the signs of a patient who isn’t managing a chronic condition effectively. The authors of an article in the journal Health Affairs suggest that healthcare providers could use data collected from patients’ smartphones to look for signs of depression or isolation, a common factor in hospital readmissions. When a patient’s behavior or vital signs reach a certain threshold, as determined by the algorithm, it triggers outreach from a home health provider, who will determine an appropriate intervention to prevent a return visit to the hospital.


Big data is proving to be a vital part of preventing hospital readmissions. One Texas hospital analyzed the records of nearly 14,000 patients to develop an algorithm that predicted the likelihood of a readmission for heart failure. Those patients with scores above the determined threshold received more intensive follow up in order to stay on track after discharge. Home healthcare is included among the interventions, with additional data from the home health providers being shared to improve care. And it’s working: The hospital has reduced readmissions for heart failure by almost half since the scoring system was implemented.

The Texas heart failure study underscores the overarching goal of using big data in home healthcare: prevention. By preventing some of the complications that can arise among those patients that utilize home healthcare, we can reduce costs and improve outcomes. Using the data to make predictions and more closely watch patients with certain behavioral or risk factors makes it easier to prevent common problems. In short, by utilizing the information we are already collecting, we can improve healthcare and save lives.

An adjunct instructor at Central Maine Community College, Kristen Hamlin is also a freelance writer on topics including lifestyle, education, and business. She is the author of Graduate! Everything You Need to Succeed After College (Capital Books), and her work has appeared in Lewiston Auburn Magazine, Young Money, USA Today, and a variety of online outlets. She has a B.A. in Communication from Stonehill College and a Master of Liberal Studies in Creative Writing from the University of Denver.

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  1. Posted August 26, 2015 at 6:40 pm | Permalink

    Thanks for the great article. The advances being made in the medical field with the use of big data is interesting.

  2. Zellemonline
    Posted April 10, 2017 at 3:12 am | Permalink

    Good post..

  3. Posted April 17, 2017 at 12:38 am | Permalink

    Thank you for sharing this informative post..

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