Healthcare Analytics

  • How do you become a nurse informaticist?

    Join Colleen Russell MSN, RN-BC, a regional clinical informaticist in operations and special services for a large healthcare system, as she shares her insights on a career in nursing informatics. With 21 years of vast experience in healthcare, she has expertise in critical care; information systems/technology; and business analysis of hospitals. This includes more than […]

  • Fundamentals of Data Analysis in Healthcare

    The recent proliferation of connected devices, sensors, and other equipment has made it almost too easy for healthcare organizations to acquire data. The potential benefits are evident: mining this data to make more informed decisions about their internal operations and patient care.

    The problem however is that just having access to data does not in itself produce results, either because it is not reliable or not easily understood. Healthcare institutions need to focus on the fundamentals of data analysis to uncover the relevant nuggets of insights which help drive decision-making.

    Bad data muddles up analytics, and bad presentation of data can put the focus on the wrong things or miss the mark altogether – it’s essential that the data be trusted and actionable. So how can healthcare facilities identify more meaningful insights that ultimately improve patient care? Here are some things to keep in mind:

    Make data available in real time
    The emergence of real-time data sources is having a dramatic impact across all industries. Data analysis no longer has to be a retrospective waiting game. It’s now enabling organizations to ask, “What’s going on right now, and what can we do about it?”

    Take for example the challenge of improving patient satisfaction scores. Hospitals have a pretty good idea of what contributes to a positive patient experience: short wait times, meaningful interaction with caregivers, and effective communication with patients and family.

    But a report on essential metrics like on-time start percentage and patient/provider contact time that arrives even a day later isn’t very helpful. It’s hard after the fact for caregivers and managers to link these stats to specific events and thus gain insight on how to do better.

    If the information is delivered in real-time by leveraging technologies such as real-time location systems (RTLS), caregivers can respond immediately to a patient who has been waiting too long and managers can better anticipate and eliminate bottlenecks in the overall patient flow. Data created by an automated RTLS can be much more accurate and timely than that entered in manually, often well after the fact and also much less accurately.

    Present data in a simple, meaningful way
    Simple, visual dashboards are essential to make the data actionable. They enable staff to more easily monitor and understand a patient’s care process in a manner that is intuitive and doesn’t require data analysis expertise, while quickly identifying pain points of procedural inefficiencies to get ahead of a problem before it occurs.

    As an example, take a look at hand hygiene procedures in a typical hospital. Healthcare-Associated Infections (HAIs) cost organizations over $35 billion annually and are a pervasive threat to patient safety. Using real-time monitoring and dashboards, however, hospitals can show staff members how they are doing individually, and show managers how the unit or hospital is doing overall – all through visual analytics. This allows for immediate action or longer term interventions such as further education or mentoring.

    Dashboards take real-time information about patients, staff and assets, and provide faster insight into how to improve the patient experience – whether by resolving issues that impact wait time or seeing what’s causing reduced staff contact time. Doctors and nurses can even access dashboards on-the-go on a tablet or wall-mounted display to gain real-time visibility into what’s happening in the OR, waiting rooms or post op rooms to make sure everything is running smoothly.

    Today, dashboards loaded with predictive analytics are becoming a reality as historical data uncovers trends. Looking at the facility’s records, organizations can better predict trends in the coming hours, weeks and months to make more informed decisions, such as using previous data on infusion pump deployments to identify how the devices should be distributed and when more will need to be ordered or rented.

    Bring on a data expert

    There’s a movement to bring self-service analytics to the masses. Business intelligence (BI) and data visualization tools like Tableau, Qlik, Microsoft and SAP are paving the way for non-technical individuals to analyze and make sense of data. But the simplicity of these solutions for users masks great sophistication on the back end both in terms of managing the data and building dashboards that non-data experts can rely on to make strategic decisions.

    Organizations need to be able to differentiate between “good” and “bad” data if they hope to avoid confusing or non-correlated results. An experienced analytics team knows that data integrity is the key to success.

    Healthcare institutions should either look into third-party vendors to handle and manage data analysis, or find a data expert to bring in-house. There are increasingly more BI teams emerging within hospitals as of late, due to the value deep data analytics provides. As the availability and applications for analytics solutions continues to grow, it’s safe to say this trend will only intensify. Often these in-house teams will partner with vendor teams who are experts in their solutions as a starting point, then take over day-to-day operation of the BI systems once launched.

    Spot and breakdown data silos
    When implementing new technologies, it’s also important to consider data silos; particularly how to avoid creating new silos and how to eliminate old ones.

    Data silos are repositories of data that are isolated from other parts of the organization. Healthcare groups should use all of the data that is available to them to drive more informed decisions and ultimately help improve patient care.

    For instance, being able to combine RTLS data with clinical performance data provides caregivers with a more complete picture of the patient journey. Ensuring that the RTLS system is integrated to the clinical system and that a common “key” exists is essential to being able to blend and analyze this type of data.

    The good news is that data silos are being broken down more and more, and larger organizations are leading the charge with tools like Tableau, Qlik and SAP. By taking advantage of the connectors in BI solutions, organizations can easily combine SQL, Oracle, Excel data and more to gain holistic discoveries.

    The amount of data flooding through hospitals today is unprecedented. But that doesn’t mean it’s being used effectively. Organizations need to develop a strategy that delivers on the basics of data analysis — understanding the data at hand, ensuring its quality, finding the relevant bits to combine and analyze, and presenting it in a way that’s easy to consume. It’s an approach that’s already delivering results in better care and higher efficiency in forward thinking healthcare organizations.

    Image:  Stuart Miles,

  • The Role of Research Informatics in the Care of Children

      Dr. Michael Miller, director of research informatics, Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, discusses how as a research informaticist, he uses data for clinical care as well as research. Dr. Miller is interested in learning how to use information entered into EHRs for the maximum benefit […]

  • A panel highlights potential pitfalls for healthcare analytics

    From left: Naveen Rao of Patchwise Labs (moderator), Nicholas Stepro of Arcadia Healthcare Solutions, Dr. Edward Ewen of Christiana Care Health System, and Hal Andrews of Digital Reasoning

    In a panel discussion at MedCity CONVERGE this week, participants shared what they’ve learned from working with big data, especially when their big data ambitions hit bumps on the road and things go pear shaped.

    Dr. Edward Ewen brought an interesting perspective to the panel discussion at the MedCity CONVERGE conference in Philadelphia this week. A physician, he now works as director of clinical data and analytics at Christiana Care Health System. Back in the day, he was one of the architects behind the assembly of Delaware’s health information exchange, the first in the country. It was no mean feat. Now, Christiana Care is “beginning our ACO journey.” He said, “I think a big part of the transition is shifting activities away from physicians and to the care team….In my experience, physicians love innovation and hate change. If you can craft tools to allow me to do what I want to do, you will see rapid adoption… We are not resistant to new tools.”

    Hal Andrews, president of healthcare at Digital Reasoning called attention to one example of a failed analytics effort that was intended to identify sepsis cases. Unfortunately, the data the customer provided for the model had already been coded as sepsis, which pretty much defeated the purpose of the exercise.

    “It’s one thing to have advanced analytics but getting it into a workflow in a timely manner is something else,” Andrews observed. “A critical part of the journey is the worklflow — delivering it to the right person who needs it when they need it.”

    Nicholas Stepro of Arcadia observed that the need to avoid interfering with workflow can have it’s downsides, too. “It’s important to listen to end users, but you cannot be a slave to them.” He used the example of electronic health records. Because health IT vendors did not want to disrupt existing workflows, they did not take risks and create something that could have been easier to use.

    In the question and answer session, an audience member wondered how  Ewen would handle a patient’s Fitbit data. “Where would you draw the line on patient-generated data? Where does that fit on the ethical line for you, as a physician? Ewen answered this way:

    “I really feel like you need to have patient consent to do that and have transparency so they know how you are using the data. Having a default opt-in or opt-out will undermine trust or slow adoption.”

    CORRECTION: An earlier version of the story had the wrong title and company association for Hal Andrews. It also misspelled Nicholas Stepro’s name.

    Photo: Meghan Uno/Breaking Media 

  • Quietly, startup Fitabase hits major Fitbit health research milestones

    Since early 2015, investigators have ResearchKit for collecting data from Apple devices. Then, ResearchStack came along for for studying Android users.

    But the research community has been able to pull in information from Fitbits and other connected, wearable devices for four years with the help of a research platform called Fitabase.

    This week, Fitbit announced that Fitabase, made by San Diego-based startup Small Steps Labs, has now collected more than 2 billion minutes of Fitbit data for research purposes. Fitabase also has supported more than 200 research projects since its 2012 founding, the company also disclosed.

    “What we’ve built is kind of the missing piece for research,” said Fitabase CEO Aaron Coleman. The platform collects and de-identifies data from Fitbit users and offers data pools to academic researchers, including many in healthcare. “This removes a lot of privacy concerns,” including those around HIPAA, Coleman said.

    “This is a technology that bridges a consumer device like Fitbit with the needs of research,” Coleman said. “Researchers are loving this new paradigm of research.”

    That’s important because millions have purchased and regularly use activity trackers. The data these wearables collect provide insights about movement, heart rate and sleep patterns that previously had not been available, plus people actually enjoy wearing their Fitbits.

    “It was really difficult to get people to use pedometers,” Coleman noted. That made it tough for researchers and clinicians alike to collect good data and, more importantly, improve health.

    “Devices help people better tailor their activities and their health,” Coleman said. “Interventions shouldn’t be the same for everyone.”

    For example, Fitabit is helping researchers determine how quickly people regain their previous level of activity following surgery. “They can tailor interventions to people who need it most,” Coleman said.

    So what about the “2 billion minutes” of Fitbit data? “We provide the researcher with de-identified data at the minute level,” Coleman explained. Each person’s activity levels can vary at different times in the day. Having this insight allows researchers — and, ultimately, healthcare professionals and caregivers — to schedule interventions when they are most likely to be effective, according to Coleman.

    Coleman pointed to a research project at Arizona State University, where Eric Hekler, director of the school’s Designing Health Lab, is applying engineering strategies to study what Hekler calls “precision behavior change,” a complement to precision medicine. Hekler and research partner Daniel Rivera, director of the ASU Control Systems Engineering Laboratory, are testing “health interventions that are adaptive and individualized, versus static and generalized,” according to a Fitbit statement.

    Coleman himself also has applied individual Fitbit data to control the level of difficulty in an app called Tappy Fit, a Flappy Birds-like mobile fitness game.

    Photos: Fitabase, Fitbit

  • How Nursing Informatics Has Improved Patient Care

    Ellen Makar, MSN, RN-BC, CCM, CPHIMS, CENP, a passionate patient and clinician advocate discusses her belief that the nation can support the quadruple aim of better health, better care, cost effective care, and improved clinician experience through thoughtful deployment of innovative technologies and proper training and support for their effective use.

  • Making Sense of Healthcare Big Data

    As the amount of data in healthcare continues to grow, data scientists are working to create solutions that address complex problems in healthcare. We are joined by Kevin Petrie, a technology evangelist at Attunity who has worked on solving problems inside and outside of healthcare with data scientists . Kevin joins us to talk about […]