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    • Creating impact: Partnering to answer big questions with health care data

      Published 17 days ago by Paul Bleicher
      • Health care data
      • Innovation
      • Collaboration
      • Analysis

      By Paul Bleicher, MD, PhD; William Crown, PhD; and Darshak Sanghavi, MD

      Welcome to the OptumLabs® blog. Here, we will take a regular look at some of the most complicated issues in health care, and explain how we’re addressing them through collaborative research and innovation.

      We'll make it accessible. We'll make it interesting. The goal is to bring you into the world of health care data with engaging but scientifically rigorous stories that make an impact on the health care system and on the health of people.

      But first, what does it take to create “impact” in health care using health care data? Impact is one of the key goals of OptumLabs. We think a lot about how to create impact and how to measure it.

      Sometimes, when you have one of the largest databases in health care, it is tempting to study a problem or do an analysis because the results will be “interesting.” But answering “interesting” questions alone doesn’t create a positive impact on people’s lives. 

      Creating impact by solving some of health care’s biggest problems with complex health data depends on a number of factors. First, you need the data and the sound science to use it. Next, you need the right expertise in health care to conceive and implement solutions once you generate important findings. And, often it takes working together with world-class organizations that have the resources and skills to implement these solutions.

      That’s OptumLabs.

      With access to health care’s richest data set of more than 200 million de-identified lives, data and analysis — with a focus on health system improvement — resides at the center of how we solve the biggest problems in health care. 

      OptumLabs staff and partners represent the world’s experts in health care. With our diverse group of partners from Mayo Clinic, AARP, AMGA, University of California Health to many others in the private and public sectors, we work collaboratively with the help of the OptumLabs Data Warehouse — our industry-leading data asset containing de-identified, linked administrative claims, medical records, and patient self-reported health information.

      Applying some of the newest artificial intelligence techniques, we look at long-term trends to better understand the health experience of individuals and its impact on the industry by pairing claims data with unique details on patient and health plan costs, demographics, health behaviors and more.

      In this blog, we'll show how that happens. Every couple of weeks, OptumLabs experts and partners will touch on some of the biggest health care problems today; like how to track and understand the many dimensions of the opioid crisis, whether Alzheimer's disease and dementias can be detected earlier, how to guard against harming patients with diabetes while trying to control their blood sugars, and much more.

      Join us on our journey to improve health and health care. Follow us on Twitter (@OptumLabs) for new post alerts.

      About the authors:

      *Paul Bleicher, MD, PhD, is the chief executive officer at OptumLabs

      *William Crown, PhD, is the chief scientific officer at OptumLabs

      *Darshak Sanghavi, MD, is the chief medical officer at OptumLabs


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    • Uncovering hidden patterns in dementia that might save lives

      Published 17 days ago by Darshak Sanghavi
      • Dementia
      • Health care data
      • Natural Language Processing (NLP)
      • Electronic Health Record (EHR)
      • Alzheimer's disease

      By Darshak Sanghavi, MD and Samantha Noderer, MA

      Decades ago, doctors had to rely on personal experience and luck to make discoveries.

      For example, around World War II, a Dutch pediatrician noticed that some of his patients, who were malnourished despite being well-fed, suddenly improved when a grain shortage hit their community.1 The doctor unknowingly stumbled on celiac disease. Once he realized that gluten in wheat had caused the illnesses, he was able to effectively treat his patients.

      Today, instead of relying on serendipity to understand mysterious conditions, we have the potential to uncover hidden trends in the massive amounts of data created by electronic medical notes and records.  

      One way OptumLabs® is trying to do this is through a collaborative program with the Global CEO Initiative on Alzheimer’s Disease.OptumLabs and several research and expert partners are exploring the roots of Alzheimer’s disease and other dementias.

      But instead of relying on chance, OptumLabs and our partners are using advanced data science techniques to organize vast information for research and visualize new, meaningful patterns in dementia.

      Paper trails to computer processing

      Over the past two decades, electronic health records (EHRs) have transformed health care by recording information digitally. Gone are the often illegible handwritten notes that made it impossible to measure or monitor trends across hundreds or thousands of patients.  Digital records now make better presentation and interpretation of large amounts of data possible.

      While we’re now able to understand what the notes say, we’re still challenged in seeing trends across large populations. That’s why we need a way to take these unstructured, narrative texts in electronic notes, and create an easier-to-analyze spreadsheet. That's where “natural language processing” comes in.

      Natural language processing (NLP) uses a combination of linguistic, statistical and exploratory methods to analyze text via computer programs and organize it for research.3 Here’s an example of how thousands of charts turn into spreadsheets via NLP.

      In the OptumLabs clinical data, NLP-derived phrases are separated into tables based on their content. The Signs, Diseases and Symptoms (SDS) table, for example, filters to a medical concept that relates to the patient and provides details such as the location on the patient’s body, features such as severity, whether it’s confirmed or denied, and other notes.

      Here we can see the NLP table filtered to fall risks. Location does not apply here.

      SDS_TERM SDS_LOCATION SDS_ATTRIBUTE SDS_SENTIMENT NOTE_SECTION
      fall risk (null) Altered (null) psychosocial
      fall risk (null) (null) negative psychosocial
      fall risk (null) (null) negative (null)
      fall risk (null) Moderate (null) (null)
      fall risk (null) (null) negative social history

       

      In short, NLP allows researchers to go from illegible handwritten notes to a structured spreadsheet of consistent data that we can review in a systematic way.

      It’s still difficult to make discoveries across a massive table with thousands or millions of rows of data. However, there are helpful techniques that build upon NLP, allowing us to visualize big data and help find patterns.

      Mapping patient journeys through clinical note visualizations

      The visual communication of data is an invaluable learning tool. It can illustrate facts and relationships in context, revealing a higher level of understanding than text alone. OptumLabs is incorporating this approach in a variety of projects underway. 

      We are using a combination of NLP and data visualization to find hidden clues in medical notes that people were developing signs of dementia before they were diagnosed. This is important because there may be measures that doctors can take to prevent or delay this progressive disease if they had warning.

      To start, we used NLP to “read” clinical notes in our de-identified EHR database from more than 40 U.S. provider practice groups or independent delivery networks that serve more than 50 million people.

      We looked back at the medical history of patients with dementia and the related terms that showed up one to four years earlier in their medical records.

      To visualize the changes over time as patients move closer to their date of diagnosis, we organized the data in an “alluvial flow” diagram. This diagram style gets its name from nature’s alluvial fans that form as sediments carried from a point of flowing water build up over time.4

      Alluvial flow diagram helps us understand the early signals that would be useful in future projects focused on prediction prevention and treatment of Alzheimers disease and related dementias

      Figure. Alluvial flow diagram of dementia-related signs and symptoms mentioned in clinical notes three years prior to an Alzheimer’s disease or dementia diagnosis. Source: OptumLabs EHR Clinical Notes Data via NLP.

      Arranged from largest to smallest, the size of the black bars at each time stamp represents the number of patients with each of these issues mentioned in their chart. The colored flows represent how many patients transition from one state to the next.

      This visualization helps us understand the early signals that would be useful in future projects focused on prediction, prevention, and treatment of Alzheimer’s disease and related dementias.

      We can use this same technique to explore patient journeys in other disease areas and use many types of data such as administrative claims data, longitudinal survey data, or disease registry information.

      As we move from big data to bigger data in health care, OptumLabs continues to explore data visualization opportunities that uncover important patterns that would otherwise go unnoticed. In turn, these patterns just might lead to discoveries that save lives.

      About the authors:

      *Darshak Sanghavi, MD, is chief medical officer at OptumLabs.

      *Samantha Noderer, MA, is health communications specialist at OptumLabs.


      1. Slate. Why Do So Many People Think They Need Gluten-Free Foods? Published Feb. 26, 2013. Accessed Oct. 16, 2017.
      2. Global CEO Initiative on Alzheimer’s Disease
      3. Nature Reviews Genetics. Mining electronic health records: towards better research applications and clinical care. Published May 2, 2012. Accessed Oct. 16, 2017.
      4. National Geographic Society. Alluvial fans. Accessed Oct. 16, 2017.

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