Next Tuesday Regina Holliday and I are among those testifying to the Health IT Policy Committee’s workgroup on Meaningful Use. Please help me decide what to submit for my testimony.
My session is Panel 2: Incorporating Patient-Generated Data in Meaningful Use of HIT. Questions:
a. What is the role of patient-generated data in improving health of individuals? What is the evidence?
b. How can patient-reported data be integrated into EHRs and the clinicians’ workflow to improve care management?
c. How can future conceptions of personal health information platforms and information tools facilitate patient-centered care, including transparency, coordinated care, patient activation, while protecting patient privacy?
d. What is the role of the patient in ensuring data in EHRs is accurate?
e. What are your recommendations for meaningful use criteria for 2013 and 2015 that are achievable by a broad spectrum of providers?
What should they hear? Comment please!
They always welcome additional thoughts beyond the specified questions, so fire away.
There is a certain degree of absurdity in the first question. There has been more than reluctance to spend money to study the impact of patient generated data. In fact there has been very significant pushback for at least 15 years. I had the great luck of working with Barbara Rimer, former director of NCI’s Division of Cancer Control and Population Sciences (DCCPS), who was among the first and only scientists ready to risk their reputation doing this kind of research. RWJF funded some very interesting research but the funding did not last long enough for us to be able to really figure out the long term impact of UGC.
What ONC should do is to help free funding to do long term studies. Otherwise we’ll remain in this insane situation, where we won’t be able to direct health professionals and policy makers to evidence-based papers showing the incredible value of patient-generated data. The perfect vicious circle!
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You’re on – I’ll quote it verbatim.
Any thoughts on the other questions?
I remember going to my university’s clinic for a cold and the doctor asking several times if I was pregnant or on drugs. While I sympathized that he often had to deal with hostile patients who wouldn’t report such information, I was annoyed that my first responses were assumed to be incorrect.
I have the same issue with any service worker who doesn’t take the time to listen to my concerns: don’t assume I’m wrong just because I’m not trained the way you are, I may actually have tried what you’re about to suggest and it didn’t work. But simultaneously, I don’t want to be the one totally responsible for a solution: I came to the expert because I couldn’t fix it on my own. Such listening and cooperation requires an attitude adjustment and training to work in that way. Studying the long-term uses of such information sounds like a move in the right direction; incorporating patient listening/input into medical education or residency might be another avenue that such studies/evidence would support.
I remember a year ago when we asked about the differences between CCD and CCRs and how each could be dealing with patient narratives.
In other words, now that many people are accepting the rich value of patient narratives and of conversations, what can be done to optimize their archival and potential use in the future? Moving away from the ICD-9 centric EHR world is a good start. Making sure that the CCR of tomorrow is designed with a full section for narrative medicine would be a lot better
Please see our policy forum published in Science last year:
http://www.sciencemag.org/cgi/content/full/324/5933/1394
It’s a new model of drug discovery that depends on patient-generated data, if the regulatory environment and patent law are modernized.
a particularly pertinent excerpt from above:
An increasingly important and influential resource is groups of patients who can access medical information on the Internet and see themselves as equal partners with—if not the primary drivers of—the medical profession in managing their health (20). Special online resources, such as Resounding Health, have recently been developed to serve this population. In a growing number of cases, patients or their relatives not only initiate, but also design and carry out, research programs that have, for example, advanced understanding and treatment of gastrointestinal stromal tumor, gastroesophageal reflux disease, autism, and the genetic disorder pseudoxanthoma elasticum (20). Most such efforts to date have been carried out as part of a “gift economy,” in which patients and their families volunteer time and effort to bypass what they consider the “lethal lag time” of professional research processes and formalisms (20).
Marc,
unfortunately your article speaks of the new paradigm in medicine but is published in a clearly old paradigm publisher, charging much money for the article. It’s not really possible to have an educated conversation as long as some have access to information while others are denied it, just for financial reasons. And that’s why the patient groups are taking charge of the research enterprise.
We just can’t have private entities gain and retain control of data that is extracted, in the first place, from our own bodies. The publisher of your article is one of those responsible for the “lethal lag time” you decry in your article :-)
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Josh Seidman, Acting Director for Meaningful Use at ONC, invites public comment on ONC’s FACA blog.
If you’re not familiar with FACA, it’s a Big Deal in the world of Government 2.0, i.e. government being open and listening to us out here. FACA is the Federal Advisory Committee Act, and as the About page on that blog says: (emphasis added)
This topic (setting policy for what’s considered “meaningful”) is of course under the Policy Committee.
OK, I admit I’m a dumb outsider, but could you give me an example of patient-generated data? Are we talking like reporting drug side effects, results of home glucometers, or what?
Hi Bev :-)
Why would I hit you? I love docs. My family wouldn’t
exist without the good/great doctors we had the luck or the effort to meet.
I believe your take on what’s considered patient-reported data is sound and would fit well with what ONC may expect.
I just hope that the meaningful use rules will be flexible enough to allow more kinds of data in the years to come. But that’s because I am on a mission to give the patient narratives the importance they deserve.
As for receiving lots of data in your work, I believe whatever amount it is will represent just a tiny amount in comparison with the genetic data that will surface more and more in the future.
Making sense of the data streams in medicine and science is expected to be the biggest issue of the next generation. It is something I hear from everywhere. Including a speaker at the National Academies of Sciences last MOnday who told us that the amount of biological/genetic data generated is growing at such exponential rate that the processing power to manage it is just not following. If that’s the case then it will be the next paradigm shift.
And before Gilles hits me, I’m a pathologist so I don’t work directly with patients, usually. But I do work with LOTS of data…..
Hi,
In case it is helpful, there are a couple of super-useful papers from the King’s Fund from 08 and 09 that describe the factors that best influence the overall experience of care (in primary care) and choice behaviour, i.e. the factors that (most) influence choice of provider.
I use these papers to explain that patient-generated data could focus on the emotional experience of care, which has a solid evidence-based to link to outcomes and safety (apologies if this is an obvious statement for you).
Some of my current work is focused on patient generated data for early warning systems for patient safety, waste identification and continuous quality improvement based on service experience.
If helpful, please don’t hesitate to contact me and I’ll send the papers and the next level of detail.
SR
Patient-generated data has high value for research purposes and this seems to be the focus of this post and comment stream. But, there’s also a need to define what patient information should be embedded in EHRs to be used for clinical decision support at the point-of-care.
I listened to a HIMSS-sponsored webinar this week (4/14) given by Louis Diamond, VP and Medical Director of Thomson Reuters Healthcare. He referenced a 1996 ACP Journal article that defined the three elements of evidence-based medicine as: 1) research evidence 2) clinical experience & judgment and 3) patient preferences. The only hints mentioned in the same article for what constitutes patient preferences are: cultural beliefs, personal values, experience and education.
We’re moving toward a health care system where more decisions will be made based on clinical decision support (CDS) systems at the point-of-care. These CDS systems benefit from mining rich data sources, which include traditional medical research and new categories of patient-generated data, including patient reported data and outcomes data generated from EHRs. So, my question is: how do we want to measure and record patient preferences for clinical purposes?
Before figuring out how to measure preferences in a way that can be applied algorithmically in a clinical decision support system, we have to decide what to measure. One obvious item to include in a patient’s record is advance directives. What else should we measure? Should we record a patient’s preference for lifestyle changes over drug therapies? What about interest in participating in experimental treatments?
The answer to these questions requires market research and the healthcare industry doesn’t have a good record for listening to its customers. The current references to patient preferences in meaningful use definitions are vague and focus on language and culture. As we continue to refine methodologies for mining repositories of outcomes data and other patient-generated data to improve targeting of therapies, shouldn’t we also be gathering intelligence on patient preferences that extend beyond these externally-defined preferences?
I know I raise a lot of questions here, but I hope I’m offering a slightly different perspective that could be helpful. And I’m always pleased with the feedback I get from posting comments on this site.
Janice
Janice, I’m not persuaded that patient preferences are “data” I would find meaningful in the EMR, although meaningful for the development of the treatment plan. I think more of the daily weight, blood pressure, sleep experience, dietary intake, bowel habits, side effect experiences, basal body temperatures, number and quality of headaches, and other measurements that patients have outside of the doctor’s office or hospital that can make all the difference to correct diagnosis and assessment of treatment effectiveness.
I agree, patient preferences are helpful for creating treatment plans, not for diagnosis. Although I could imagine a case where data that were classified as “preferences” might be relevant to diagnosis, but I have a way of finding points of connections between things!
This discussion about patient preferences reminds me of the amazing amount of bias built in the system.
I think it is great to collect patient preferences. But just as important we should be collecting in parallel the physician preferences. Not doing so will keep perpetuating the false idea that doctors don’t have very strong built-in biases that impact the kind and quality of care the patients receive. As Danny Sands says it is time to “move from information asymmetry to information symmetry”. Participatory Medicine is about that too.
I would suggest at least two categories of patient-supplied data that should be acknowledged/accommodated:
1)Test results and other data introduced by the patient produced by third parties that are not connected to the HIT infrastructure (e.g., heart scans, genetic test results ala 23andMe,prescription history from insurance EOB’s etc)
2) Patient self-monitoring/diary (weight, BP, sleep quality, mood, dietary reactions, etc..) and being able to differentiate between offline records (paper and pencil notes by patient) and digitized sources (glucose meter readings, MOOD247 records, WiiFit weight data, Fitbit activity data, wristwatch heart monitor data etc..)
Ignoring the sources above, when present, will ensure that we will always be playing catch-up to data sources in the health industry. Let’s get them in now.