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The Sexiest Data in Health IT: Datapalooza 2017

Posted on May 15, 2017 I Written By

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. twitter: @coherencemed

The data at this conference was the Best Data. The Biggest Data. No one has better data than this conference.

The sexiest data in all of healthIT was highlighted in Washington DC at Datapalooza April 27-28, 2017.  One of the main themes was how to deal with social determinants of health and the value of that data.  Sachin H. Jain, MD of Caremore Health reminded us that “If a patient doesn’t have food at home waiting for them they won’t get better” social data needs to be in the equation. Some of the chatter on the subject of healthcare reform has been criticism that providing mandatory coverage hasn’t always been paired with knowledge of the area. If a patient qualifies for Medicaid and has a lower paying job how can they afford to miss work and get care for their health issues?
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Rural areas also have access issues. Patient “Charles” works full time during the week and qualifies for Medicaid. He can’t afford to miss a lot of work but needs a half a day to get treatments which affect his ability to work. There is no public transportation in his town to the hospital in a city an hour and a half away. Charles can’t afford the gas or unpaid time off work for his treatment.

Urban patient “Haley” returns to her local ER department more than once a week with Asthma attacks.  Her treatments are failing because she lives in an apartment with mold in the walls. As Craig Kartchner from the Intermountain Healthcare team responded to the #datapalooza  hashtag online- These can be the most difficult things to change.

The 2016 report to Congress addresses the difficulty of the intersection between social factors and providing quality healthcare in terms of Social Determinants of Health:

“If beneficiaries with social risk factors have worse health outcomes because the providers they see provide low quality care, value based purchasing could be a powerful tool to drive improvements in care and reduce health disparities. However, if beneficiaries with social risk factors have worse health outcomes because of elements beyond the quality of care provided, such as the social risk factors themselves, value based payment models could do just the opposite. If providers have limited ability to influence health outcomes for beneficiaries with social risk factors, they may become reluctant to care for beneficiaries with social risk factors, out of fear of incurring penalties due to factors they have limited ability to influence.”

Innovaccer just launched a free tool to help care teams track and monitor Medicare advantage plans. I went to their website and looked at my county and found data about the strengths in Salt Lake where I’m located. They included:

  • Low prevalence of smoking
  • Low Unemployed Percentage
  • Low prevalence of physically inactive adults

Challenges for my area?

  • Low graduation rate
  • High average of daily Air pollution
  • High income inequality
  • High Violent crime rate per 100,000 population

Salt Lake actually has some really bad inversion problems during the winter months and some days the particulate matter in the air creates problems for respiratory problems. During the 2016-2017 winter there were 18 days of red air quality and 28 days of yellow air quality. A smart solution for addressing social determinants of health that negatively impact patients in this area could be addressing decreasing air pollution through increased public transportation. Healthcare systems will see an increase in cost of care during those times and long term population health challenges can emerge. You can look at your county after you enter your email address on their site. This kind of social data visualization can give high level insights into the social factors your population faces.

One of the themes of HealthDataPalooza was how to use system change to navigate the intersection between taking care of patients and not finding way to exclude groups. During his panel discussion of predictive analytics, Craig Monson the medical director for analytics and reporting discussed how “data analytics is the shiny new toy of healthcare.”    In addition to winning the unofficial datapalooza award for the most quotes and one liners – Craig presented the Clinical Risk Prediction Initiative (CRISPI).  This is a multi variable logistic regression model with data from the Atrius health data warehouse. His questions for systems to remember in their data analysis selection are “Who is the population you are serving? What is the outcome you need? What is the intervention you should implement?”

Warning- Craig reminds us that in a world of increasing sexy artificial intelligence coding a lot of the value analysis can be done with regression. Based on that statement alone I think he can be trusted. I still need to see his data.

CRISPI analyzed the relative utility of certain types of data, and didn’t have a large jump in utility when adding Social Determinant Data. This data was one of the most popular data sets during Datapalooza discussions but the reality of making actionable insights into system improvement? Craig’s analysis said it was lacking. Does this mean social determinant data isn’t significant or that it needs to be handled with a combination of traditional modeling and other methods?  Craig’s assertion seemed to fly in the face of the hot new trend of Social Determinants of Health data from the surface.

Do we have too much data or the wrong use of the data? Most of the companies investing into this space used data sources outside the traditional definition to help create solutions with social determinate of health and Patient outcomes. They differed in how they analyzed social determinant data. Traditional data sources for the social determinants of health are well defined within the public health research.  The conditions in which you work and live impact your health.

Datapalooza had some of the greatest minds in data analytics and speakers addressed gaps in data usefulness. Knowing that a certain large county wide population has a problem with air quality might not be enough to improve patient outcomes. There is need for analysis of traditional data sources in this realm and how they can get meaningful impact for patients and communities. Healthcare innovators need to look at different data sources.  Nick Dawson, Executive director of Johns-Hopkins Sibley Innovation Hub responded to the conversation about food at home with the data about Washington DC.  “DC like many cities has open public data on food scarcity. But it’s not part of a clinical record. The two datasets never touch.” Data about food scarcity can help hospital systems collaborate with SNAP and Government as well as local food programs. Dawson leads an innovation lab at Johns Hopkins Sibley where managers, directors, VPs and C Suite leaders are responsible for working with 4 innovation projects each year.

Audun Utengen, the Co Founder of Symplur said “There’s so much gold in the social media data if you choose to see it.” Social data available online helps providers meet patients where they are and collect valuable data.  Social media data is another source to collect data about patient preferences and interactions for reaching healthcare populations providers are trying to serve. With so much data available sorting through relevant and helpful data provides a new challenge for healthcare systems and providers.

New Data sources can be paired with a consultative model for improving the intersection of accountable care and lack of access due to social factors. We have more sophisticated analytic tools than ever for providing high value care in the intersection between provider responsibility and social collaboration. This proactive collaboration needs to occur on local and national levels.  “It’s the social determinants of health and the behavioral aspects that we need to fund and will change healthcare” we were reminded. Finding local community programs that have success and helping develop a strategy for approaching Social Determinants of Health is on the mind of healthIT professionals.

A number of companies examine data from sources such as social media and internet usage or behavioral data to design improvements for social determinants of health outcomes.   They seek to bridge the gaps mentioned by Dawson. Data sets exist that could help build programs for social determinants of health.  Mandi Bishop started Lifely Insights centered around building custom community plans with behavioral insights into social determinant data. Health in all Policies is a government initiative supporting increased structure and guidelines in these areas. They support local and State initiatives with a focus on prevention.

I’m looking forward to seeing how the data landscape evolves this year. Government Challenges such as the Healthy Behavior Data Challenge launched at Datapalooza will help fund great improvements. All the data people will get together and determine meaningful data sets for building programs addressing the social determinants of health. They will have visualization tools with Tableau. They will find ways to get food to patients at home so those patients will get better. Programs will find a way to get care to rural patients with financial difficulty and build safe housing.

From a healthcare delivery perspective the idea of collaborating about data models can help improve community health and decrease provider and payer cost. The social determinants of health can cost healthcare organizations more money than data modeling and proactive community collaboration.

Great regressions, saving money and improving outcomes?

That is Datapalooza.

Exploring the Role of Clinical Documentation: a Step Toward EHRs for Learning

Posted on January 19, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

We need more clinicians weighing in on the design of the tools they use, so I was gratified to see a policy paper from the American College of Physicians about EHRs this week. In a sporadic and tentative manner, the paper recognizes that our digital tools for clinical documentation are part of a universal health care system that requires attention to workflow, care coordination, outcomes, and research needs.

The strong points of this paper include:

  • A critique of interfaces that hobble the natural thought processes of the clinician trying to record an encounter

  • A powerful call to direct record-keeping away from billing and regulatory requirements, toward better patient care

  • An endorsement of patient access to records (recommendation 6 under Clinical Documentation) and even more impressively, the incorporation of patient-generated data into clinical practice (recommendation 5 under EHR System Design)

  • A nod toward provenance (recommendation 3 under EHR System Design), which tells viewers who entered data and when, thus allowing them to judge its accuracy

Although the authors share my interests in data sharing and making data available for research, their overarching vision is of an electronic record that supports critical thinking. An EHR should permit the doctor to record ideas about a patient’s condition as naturally as they emerge from his or her head. And it should support other care-takers in making treatment decisions.

That’s a fine goal in itself, but I wish the authors also laid out a clearer vision of records within a learning health care system. Currently a popular buzzword, a learning health care system collects data from clinicians, patients, and the general population to look for evidence and correlations that can improve the delivery of health care. The learning system can determine the prevalence of health disorders in an area, pick out which people are most at risk, find out how well treatments work, etc. It is often called a “closed loop system” because it can draw on information generated from within the system to change course quickly.

So at the start of the policy paper I was disappointed to read, “The primary goal of EHR-generated documentation should be concise, history-rich notes that reflect the information gathered and are used to develop an impression, a diagnostic and/or treatment plan, and recommended follow-up.” What about supporting workflows? Facilitating continuous, integrated care such as in a patient-centered medical home? Mining data for new treatments and interventions? Interfacing with personal health and fitness devices?

Fortunately, the authors massage their initial claim by the time they reach their first policy recommendation under Clinical Documentation: “The primary purpose of clinical documentation should be to support patient care and improve clinical outcomes through enhanced communication.” The primary purpose gets even better later on: “As value-based care and accountable care models grow, the primary purpose of the EHR should remain the facilitation of seamless patient care to improve outcomes while contributing to data collection that supports necessary analyses.”

One benefit of reading this paper is its perspective on how medical records evolved to their current state. It notes a swelling over the decades in the length of notes and the time spent on them, “the increased documentation arguably not improving patient care.” Furthermore, it details how the demands of billing drove modern documentation, blaming this foremost on CMS’s “issuance of the evaluation and management (E&M) guidelines in 1995 and 1997.” I suspect that private insurers are just as culpable. In any case, the distortion of diagnosis in the pursuit of payments hasn’t worked well for either goal: 40% of diagnoses are wrongly coded.

The pressures of defensive medicine also reveal the excessively narrow view of the EHR currently as an archive rather than a resource.

The article calls for each discipline to set standards for its own documentation. I think this could help doctors use fields consistently in structured documentation. But although the authors endorse the use of macros, templates, and (with care) copy/forward, they are distinctly unfriendly toward structured data. Their distemper stems from the tendency of structured interfaces to disrupt the doctor’s thinking–the presevervation of which, remember, is their main concern–and to make him jump around from field to field in an unnatural way.

Yet the authors recognize that structured data is needed “for measurement of quality, public health reporting, research, and regulatory compliance” and state in their conclusion: “Vendors need to improve the ability of systems to capture and manage structured data.” We need structured data for our learning health care system, and we can’t wait for natural language processing to evolve to the point where it can reliably extract the necessary elements of a document. But a more generous vision could resolve the dilemma.

Certainly, current systems don’t handle structured data well. For instance, the article restates the well-known problem of redundant data entry, particularly to meet regulatory requirements, a problem that could be solved with minimally intelligent EHR processing engines. The interactive features available on modern mobile devices and web interfaces could also let the clinician enter data in any manner suited to her thinking, imposing structure as she goes, instead of forcing her into a rigid order of data entry chosen by the programmer.

Already, Modernizing Medicine claims to make structured data as easy to enter as writing in a paper chart. As I cover in another article, they are not yet a general solution, but work only with a few fields that deal with a distinct set of health conditions. The tool is a model for what we can do in the future, though.

The common problem of physicians copying observations from a previous encounter and pasting them into the current encounter is a trivial technical failure. On the web, when I want to cite material from a previous article, I don’t copy it and paste it in. I insert a hyperlink, I did in the previous paragraph. EHRs could similarly make reporting simple and accurate by linking to previous encounters where relevant.

The ACP recommendations are sensible and well-informed. If implemented by practitioners and EHR developers who keep the larger goals of health care in mind, they can help jump over the chasm between where EHRs and documentation are today, and where we need them to be.

Full Disclosure: Modernizing Medicine is an advertiser on this site.