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For over two years now, thought leaders in healthcare IT have been telling us that healthcare data analytics, also known as Big Data Healthcare, are going to revolutionize the entire healthcare industry. Access to a larger quantity of personal healthcare data plus an improved ability to analyze these data sets are supposed to influence how consumers shop for healthcare, how healthcare is delivered, and even the kind of care and medicine consumers receive.
So where are we in this quest for better, more personalized healthcare based on healthcare data analytics? RTS Labs takes a look at three specific ways to apply big data to healthcare delivery and customer service that medical organizations are using right now.
National and local patient safety scorecards are starting to gain traction both with hospitals and with patients. These scorecards track measurements such as surgeon complication rates, Medicare readmission rates, and other objective scores that show patients how well their local hospital is performing compared to other hospitals that perform the same procedures and services. Depending on which scorecard a healthcare consumer is using, these scorecards may aggregate healthcare data from many different sources, such as voluntary surveys and government Medicare ratings.
One of the best known national scorecards is the Hospital Safety Score. The Hospital Safety Score was created and is administered by The Leapfrog Group, “a nonprofit organization committed to driving quality, safety, and transparency in the U.S. health system.” Hospitals receive a simple one-letter rating of A, B, C, D, or F. Healthcare consumers can see the ratings and look into how each hospital earned that score. The ratings look at areas such as new infection rates, readmission rates, accidental cuts and tears, and patient complaints, to name a few examples.
The Leapfrog Group claims on their website that their survey is “the gold standard for comparing hospitals’ performance”. However, since participation in the survey is voluntary, some critics claim the survey doesn’t go far enough, since it relies on self-reporting from hospitals. Despite any shortcomings, some hospitals will feel pressure to participate in the survey anyway, since their absence from the list may be viewed suspiciously by healthcare consumers. The more hospitals that participate, the better the survey will work and give real information and choices to patients.
The Virginia Hospital and Healthcare Association recently teamed up with RTS Labs to create Virginia’s first online patient quality and safety scorecard. The scorecards get their information from The Centers for Medicare & Medicaid Services and the Virginia Department of Health. It’s the first site to aggregate healthcare data for hospitals across Virginia. ProPublica also has a scorecard devoted to reporting surgeons’ complication and death rates. This site lets consumers look by hospital and by individual surgeon. Again, a site like this can be tricky, because for surgeons that take more high-risk cases, their rates may skew lower but not truly tell the whole story.
The goal of making this information public and accessible is to help patients make more informed choices about which hospitals and outpatient centers they choose to get their healthcare from. When you combine these kinds of scorecards with online healthcare price surveys, such as the price comparison tool just launched in California or the online healthcare pricing tool in Massachusetts that has gained national attention, consumers and health insurance companies can look for hospitals that have the best outcomes at more reasonable prices. Medical data analytics are making it possible to apply basic free-market principles and transparency to the healthcare industry. Once healthcare consumers get in the habit of being able to comparison shop for hospitals and read Facebook healthcare reviews the way they would when shopping for anything else, the competition between hospitals should drive down prices and increase quality in theory.
The government’s new five-star quality rating system is supposed to make it easier for patients to compare hospitals. However, this new rating system raises new challenges for hospitals, because the ratings are completely based on patient surveys. That means that hospitals are at the mercy of their patients’ perceptions of not only their actual care but how they feel their care should have gone. While patient input is extremely important, especially when measuring things like comfort and bedside manner, if someone isn’t feeling well to begin with, their responses on the survey may skew towards lower ratings.
That’s where healthcare data analytics can really help hospitals. By aggregating healthcare data from patient surveys on a continual basis, hospitals can spot trends, look for problematic patterns, and hopefully fix recurring problems faster – instead of being surprised when the Medicare ratings come out. In the course of the year, monitoring and responding to these kinds of patient concerns could help boost Medicare rating scales overall – which in turn boosts government reimbursements.
One of the best ways to aggregate clinical data is through a data analytics dashboard. By creating an analytics dashboard that’s accessible and easy to use, everyone who needs access to quality of care scores, from IT and medical staff to executives and quality control auditors, will have access to the data they need in one, convenient place. Having a convenient way to access up-to-date information makes it easier for the hospital or medical organization in question to conduct regular performance reviews and audits, which can keep the organization as a whole on track.
There are a number of business intelligence software tools available on the market, many of which can be customized to clients’ needs. Or, for specialized industries such as healthcare, sometimes working with a tech consulting company to create custom data analytics software can lead to better security and performance.
The final step in healthcare data analytics is to use what the healthcare data is telling us to improve patient outcomes and quality of life, or the practice known as applied health analytics.
One hot trend people are discussing is personal health data that’s gathered by smartphone apps and wearable technology. As RTS Labs discusses in their blog, “Why Your Doctor Wants a Peek at Your Smartphone”, wearable technology and fitness apps can give doctors, nurses, and other medical professionals a rare and honest glimpse into patients’ everyday lives. A patient can lie about what they ate or how much they exercised on a questionnaire, but it’s harder to fool a smartphone app or a Fitbit®. Getting access to personal health and fitness data like this could favorably impact follow-up care, too, as medical pros are better able to check on and communicate with patients after they are discharged from care. Patients may be able to address follow-up care without having to go back to the doctor’s office or hospital, saving them time and saving the clinic or hospital money. And ultimately, better follow-up care is key to lowering hospital readmission rates.
Insurance companies and hospitals are also hoping to work together to use medical data analytics to identify and better manage high-risk, high-cost patients. Insurance companies want to identify high-cost patients to see if they would benefit from early interventions that could keep patients in better health and reduce medical costs later. Another sophisticated use of this kind of healthcare data could be to use algorithms with ICU patients to foresee who is more at risk for readmission. Medical staff can then take different, proactive measures as necessary to try to lower that risk of readmission, such as precise discharge instructions, different prescriptions, or a specific follow-up visit schedule, as just a few examples. The California Health Foundation’s article, “Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients”, offers six specific uses for healthcare data analytics, such as being able to predict if a patient’s condition in the ICU will improve or worsen.
Another example of turning data sets into actionable data is the myHDL mobile app that RTS Labs worked on with Health Diagnostic Laboratory, now the newly formed True Health Diagnostics. This first-of-its-kind its kind mobile health app lets doctors and patients track lab results over time, communicate with each other, and join support communities with people with similar health conditions. This kind of interactive health data creates a reliable health history for patients and helps patients be more engaged with their care and their health outcomes.
The practice of using big data from healthcare to improve patient care and healthcare deliverables is still in its infancy. We’ve only scratched the surface of what this clinical data can be used for and how health care analytics are going to change the face of healthcare as we know it.
The excitement about the possibilities that healthcare data analytics hold are only tempered by the technology we have available to us and by the privacy and ethical concerns of how this personal health data is used. We’ll be tackling the security and ethical issues surrounding personal health data in a future blog, so stay tuned.
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