If you like creative online video education, you’ll enjoy this Katy Perry “Roar” parody by ZDoggMD. Ok, it might not be all that educational, but it’s pretty hilarious. I think we all have someone in our life with this problem it seems. So, I’m sure that many of you will relate. Enjoy the video embedded below.
One of the biggest disappointments at this year’s Health Datapalooza (which I found disappointing overall) was the continued impasse presented to patients who, bolstered by the best thinking in health care as well as Federal laws and regulations, ask for health data stored about them by doctors and other institutions.
Activists such as Regina Holliday and e-Patient Dave proved years ago that giving patients information and involving them in decisions will save lives. The Society for Participatory Medicine enshrines the principle. But the best witnesses for patient empowerment are the thousands of anonymous patients, spouses, parents, and children quietly trundling folders with their own records through the halls of hospitals, building up more knowledge of their chronic conditions than any professional clinician possesses, and calmly but inflexibly insisting on being equal partners with those who treat them.
There were plenty of high-minded words at the Datapalooza about patient rights to data. It was recognized as a key element of patient empowerment (or “activation,” as the more timid speakers liked to say) as well as an aid to better care. An online petition backed by an impressive array of health reformers is collecting signatures (whom someone will presumably look at) and encourages activists to speak up about this topic on July 4. HHS announced that anyone denied access to data to which the law gives her a right can submit an informal report to firstname.lastname@example.org.
Although occasional mention was made of personal health records (PHRs), most of the constant discussion about interoperability stayed on the safe topic of provider-to-provider data exchange. Keeping data with health care providers leads to all sorts of contorted practices. For instance, patient matching and obtaining consent are some of the most difficult challenges facing health IT in the U.S., all caused by keeping data with providers instead of the patients themselves.
The industry’s slowness to appreciate patient-generated data is also frustrating. Certainly, the health IT field needs to do a lot more to prepare data for use: consumer device manufacturers must assure clinicians of the devices’ accuracy, and researchers need to provide useful analytics that clinicians can plug in to their electronic systems. Still, doctors are demonstrating a disappointing lack of creativity in the face of this revolutionary source of information. It’s all to easy to carp about accuracy (after all, lab tests have limited accuracy as well) or just to state that you don’t know what to do with the data.
I heard about recent progress at the UK’s National Health Service from Brian Ahier, who is the only person I know who can explain the nuances of extensions to FHIR resources while actively using both his laptop and his cell phone at the same time. Ahier heard at a UK-US Bootcamp before the Datapalooza that the NHS has given 97% of its patients access to their records.
But there’s a bit of a caution around that statistic: only one-fifth of the patients have taken advantage of this right. This doesn’t bother me. First of all, one-fifth of the population with access to their personal records would be a dizzying accomplishment for most countries, including the U.S. Second, few people need access to records until some major problem arises, such as the need to see a specialist. They probably feel relieved to know the records will be there when needed.
Another aspect of patient control over data is research. The standard researcher-centered model is seen as increasingly paternalistic, driving patients away. They’re not impressed with being told that some study will benefit people like them–they want to tell researchers what really matters to them as sufferers, and hear more about the study as it goes along. Researchers are frantic to reverse a situation where most studies fail simply because they can’t sign up enough subjects.
The Patient-Centered Outcomes Research Institute (PCORI) is one of the progressive institutions in health care who understand that giving patients more of a say will be increasingly important for signing up patients in the first place, as well doing research of value to them. Its PCORnet combines traditional research databases with databases maintained by patient advocacy groups. Each member network can create its own policies for getting consent, which allows researchers to bend with the needs of their research subjects.
OpenClinica, the open source clinical research platform, just announced the release of an app that may contribute to the goals of taking input from patients and binding them closer to the research endeavor.
Public health officials also recognize the sensibilities of the people they monitor. At a panel on data about low-income people, speakers stressed the importance of collecting data in a respectful way that doesn’t make people feel they’re being spied on or could be punished for their behavior.
Let’s talk a minute about health care costs, if only because doctors and insurers don’t want to. (Some doctors are prohibited by their employers from telling patients how much a recommended procedure will cost, supposedly because they don’t want costs to intrude on what should ideally be a clinical decision. This is changing with the increase in deductibles, but often the doctors don’t even know what the final cost will be after insurance.)
One app so admired by the Datapalooza team that they allowed the company to demonstrate its product on the main stage during keynote time was Sensentia. This product everybody is so impressed with takes in information from health plans to allow patients as well as the staff at health care providers to quickly find the health plan benefits for a procedure. (I recently covered another company doing similar work with insurance and costs.)
Sensentia is a neat product, I am willing to aver. It accepts natural language queries, crunches the data about health plans and insurers, and returns the actual health plan benefits for a treatment. Of course, I know the cost of flying from Boston to San Francisco after six clicks in my browser, even though the calculations that go into offering me a price are at least as complicated as those run by health plans. One may be shocked to hear that that current phone calls to an insurer cost $3-$10. This is the state of health care–it costs more than five bucks on average for a doctor just to find out how much it will cost to offer his own service.
A panel on patient-generated data reported more barriers than successes in getting doctors to work with data from patient devices and reports from everyday life. Another panel about improving quality measures culminated in the moderator admitting that more patients use Yelp than anything else to choose providers–and that it works pretty well for them.
For me that was the conference’s low point, and a moment of despairing cynicism that doesn’t reflect the mood of the conference or the health care field as a whole. Truly, if Yelp could solve our quality problems, we wouldn’t need a Datapalooza or the richness of data analysis it highlights. But I think reformers need more strategies to leap the hurdles we’re facing and implement the vision we all share.
Two commonplaces heard in the health IT field are that the data in EHRs is aimed at billing, and that billing data is unreliable input to clinical decision support or other clinically related analytics. These statements form two premises to a syllogism for which you can fill in the conclusion. But at two conferences last week–the Health Datapalooza and the Health Privacy Summit–speakers indicated that smart analysis can derive a lot of value from claims data.
The Healthcare Cost and Utilization Project (HCUP), run by the government’s Agency for Healthcare Research and Quality (AHRQ), is based on hospital release data. Major elements include the payer, diagnoses, procedures, charges, length of stay, etc. along with potentially richer information such as patients’ ages, genders, and income levels. A separate Clinical Content Enhancement Toolkit does allow states to add clinical data, while American Hospital Association Linkage Files let hospitals upload data about their facilities.
But basically. HCUP data revolves around the claims from all-payer databases. It is collected currently from 47 states, and varies on a state-by-state basis depending on what data they allow to be released. HCUP goes back to 2006 and powers a lot of research, notably to improve outreach to underserved racial and ethnic groups.
During an interview at the Health Privacy Summit, Lucia Savage, Chief Privacy Officer at ONC, mentioned that one can use claims data to determine what treatments doctors offer for various conditions (such as mammograms, which tend to be underused, and antibiotics, which tend to be overused). Thus, analysts can target providers who fail to adhere to standards of care and theoretically improve outcomes.
M1, a large data analytics company serving a number of industries, bases a number of products in the health care space on claims data. For instance, medical device companies contract with M1 to find out which devices doctors are ordering. Insurance companies use it to sniff out fraud.
M1’s business model, incidentally, is a bit different from that pursued by most analytics organizations in the health care arena. Most firms contract with some institution–an insurer, for instance–to analyze its data and provide it with unique findings. But M1 goes around buying up data from multiple institutions and combining it for deeper insights. It then sells results back to these institutions, often paying out taking in payment from the same company.
In short, smart organizations are shelling out money for data about billing and claims. It looks like, if you have a lot of this data, you can reliably lower costs, improve marketing, and–most important of all–improve care. But we mustn’t lose sight of the serious limitations and weaknesses of this data.
A scandalously amount of it is clinical just wrong. Doctors “upcode” to extract the largest possible reimbursement for what they treat. A number of them go further and assign codes that have no justification whatsoever. And that doesn’t even count outright fraud, which reaches into the billions of dollars each year and therefore must leave a lot of bad data in the system.
Data is atomized, each claim standing on its own. A researcher will find it difficult to impossible (if patient identifiers are totally stripped out) to trace a sequence of visits that tell you about the progress of treatment.
Data is relatively impoverished. Clinical records flesh out the diagnosis with related conditions, demographic information, and other things that make the difference between correct and incorrect treatments.
But on the other hand, to go beyond billing data and reach the data utopia that reformers dream about, we’d have to slurp up a lot of complex and sensitive patient data. This has pitfalls of its own. Little clinical data is structured, and the doctors who do take the effort to enter it into structured fields do so inconsistently. Privacy concerns also raise their threatening heads when you get deep into patient conditions and demographics. So perhaps we should see how far we can get with claims data.
There seems to be a healthy competition among companies to pump up their staff’s participation in “wellness” programs. One recent poll of employers found that 60% offer incentives of some type for healthy behavior. But beyond anecdotal evidence, rigorous studies are casting doubt on the effectiveness of wellness programs. And that makes me wonder: will doctors face the same problems while trying to bend the health cost curve?
Talk to employers, and most will gush with enthusiasm over their wellness programs, which are spreading far and wide in the U.S. But at last week’s Health Privacy Summit, law professor Lindsay Wiley reviewed the literature and found a very different picture. A clinical study of a weight loss program found that the average weight loss it produced was…one pound. Other studies found that if people lost weight or stopped tobacco use to earn financial incentives, they “bounced back” and regained the weight or resumed smoking. Often, weight gains were more than what had been lost.
We should acknowledge that wellness programs take many forms, some showing more promise than others. General initiatives to put healthier food in cafeterias and vending machines seem benign. Incentives for employees to join a gym and get regular medical check-ups also seem successful in meeting those goals. But what health reformers really hope for–whether in the workplace or through clinical efforts such as Accountable Care Organizations–is thorough-going behavior change. Long-term lifestyle practices that reduce the incidence of diabetes, heart failure, etc. are what both employers and insurers seek in order to reduce the costs of health care plans. And that’s just where the wellness programs haven’t demonstrated results.
The panel in which Wiley participated also covered other weaknesses of fitness programs. Some 18% tie incentives to the output of fitness devices or other biomarkers, and nearly 50% intend to move in that direction in the future. But many consumer devices are inaccurate. Furthermore, many are easy to deceive by employees determined to game the system. There are even videos online that tell people how to manipulate results.
Concerns over privacy and discrimination run through these systems. Any device given by an employer to an employee–a Fitbit just as much as a laptop–remains the property of the employer, and so does the data on that device. Do we want employers to have unrestricted access to everything a device says about us? Walls keeping personal data in fitness programs away from human resource departments are insubstantial and easy to penetrate.
It’s disappointing that we lack firm evidence that wellness programs can lead to life-enhancing behavioral changes. But the lessons we learned from employees also have upsetting implications for the rest of the health care system. The whole premise of risk sharing (such as in ACOs) is that clinicians can persuade their patients to reject a lifetime of unhealthy eating, smoking, and sedentary habits. What if they can’t? Are clinicians being set up to fail? Bob Kocher suggested as much in a panel at the recent Health Datapalooza, when he pointed out that many patients have serious and multiple chronic conditions that are hard to turn around.
Some ACOs are demonstrating cost savings through low-hanging fruit, such as calling up patients to remind them to come to appointments or take their medications. These can make a big difference, but after we’ve exhausted the benefits of these simple interventions, how can we move to the next level of changing lifestyles?
I do have hope for lifestyle change. It will involve a lot more than crude incentives. It will require a clinician or other professional to form a trust relationship with a patient. It will require a lot of education. And technology can probably help. If integrated into a clear, individualized plan that the patient buys into, communications technologies and sensors can help patients stick to their commitments.
Health is also a community effort–a key point lost in the rush to wellness programs. Setting up such programs implies that each person is responsible for his or her own health. It moves the blame from social trends in food, transportation, air quality, etc. to individuals.
The workplace is where we spend an increasing number of our waking hours, so it makes sense to put the workplace in the spotlight of health care efforts. But we must let wellness programs whitewash employers’ responsibility for increasing stress, providing ergonomically destructive environments, or disrupting employees’ sleep. A recent Dilbert cartoon is relevant here. Let’s each take responsibility–as employers, as clinicians, as public health officials, and as individuals–for the things over which we have control.
If you look at the policy statements issued by ONC, it sounds as though the organization is a big fan of putting behavioral health IT on the same footing as other aspects of care. As the agency itself points out, 46% of Americans will have a mental health disorder over the course of their lifetime, and 26% of Americans aged 18 and older live with a mental health disorder in any given year, which makes it imperative to address such issues systematically.
But as things stand, behavioral health IT initiatives aren’t likely to go far. True, ONC has encouraged behavioral health stakeholders on integrating their data with primary care data, stressed the value of using EMRs for consent management, supported the development of behavioral health clinical quality measures and even offered vendor guidelines on creating certified EMR tech for providers ineligible for Meaningful Use. But ONC hasn’t actually suggested that these folks deserve to be integrated into the MU program. And not too surprisingly, given their ineligibility for incentive checks, few mental health providers have invested in EMRs.
However, a couple of House lawmakers who seem pretty committed to changing the status quo are on the case. Last week, Reps. Tim Murphy (R-Pa.) and Eddie Bernice Johnson (D-Texas) have reintroduced a bill which would include a new set of behavioral health and substance abuse providers on the list of those eligible for Meaningful Use incentives.
The bill, “Helping Families in Mental Health Crisis Act,” would make clinical psychologists and licensed social workers eligible to get MU payments. What’s more, it would make mental health treatment facilities, psychiatric hospitals and substance abuse mistreatment facilities eligible for incentives.
Supporters like the Behavioral Health IT Coalition say such an expansion could provide many benefits, including integration of psych and mental health in primary care, improved ability of hospital EDs to triage patients and reduction of adverse drug-to-drug interactions and needless duplicative tests. Also, with interoperable healthcare data on the national agenda, one would think that bringing a very large and important sector into the digital fold would be an obvious move.
So as I see it, making it possible for behavioral health and other medical providers can share data is simply a no-brainer. But that can’t happen until these providers implement EMRs. And as previous experience has demonstrated, that’s not going to happen until some version of Meaningful Use incentives are available to them.
I imagine that the bill has faltered largely over the cost of implementing it. While I haven’t seen an estimate of what it would cost to expand eligibility to these new parties, I admit it’s likely to be very substantial. But right now the U.S. health system is bearing the cost of poorly coordinated care administered to about one-quarter of all U.S. adults over age 18. That’s got to be worse.
Some doctors — and a goodly number of consumers, too — argue that the use of EMRs inevitably impairs the relationship between doctors and patients. After all, it’s just common sense that forcing a doctor to glue herself to the keyboard during an encounter undercuts that doctor’s ability to assess the patient, critics say.
Of course, EMR vendors don’t necessarily agree. And some researchers don’t share that view either. But having reviewed some comments by a firm studying physician EMR use, and the argument an EMR vendor made that screen-itis doesn’t worry docs, it seems to me that the “lack of face time” complaint remains an important one.
Consider how some analysts are approaching the issue. While admitting that one-third to one-half of the time doctors spend with patients is spent using an EMR, and that physicians have been complaining about this extensively over the past several years, doctors are at least using these systems more efficiently, reports James Avallone, Director of Physician Research, who spoke with EHRIntelligence.com.
What’s important is that doctors are getting adjusted to using EMRs, Avallone suggests:
Whether [time spent with EMRs] is too much or too little, it’s difficult for us to say from our perspective…It’s certainly something that physicians are getting used to as it becomes more ingrained in their day-to-day behaviors. They’ve had more time to streamline workflow and that’s something that we’re seeing in terms of how these devices are being used at the point of care.
Another attempt to minimize the impact of EMRs on patient encounters comes from ambulatory EMR vendor NueMD. In a recent blog post, the editor quoted a study suggesting that other issues were far more important to doctors:
According to a 2013 study published in Health Affairs, only 25.8 percent of physicians reported that EHRs were threatening the doctor-patient relationship. Administrative burdens like the ICD-10 transition and HIPAA compliance regulations, on the other hand, were noted by more than 41 percent of those surveyed.
It’s certainly true that doctors worry about HIPAA and ICD-10 compliance, and that they could threaten the patient relationship, but only to the extent that they affect the practice overall. Meanwhile, if one in four respondents to the Health Affairs study said that EMRs were a threat to patient relationships, that should be taken quite seriously.
Of course, both of the entities quoted in this story are entitled to their perspective. And yes, there are clearly benefits to physician use of EMRs, especially once they become adjusted to the interface and workflow.
But if this quick sample of opinions is any indication, the healthcare industry as a whole seems to be blowing past physicians’ (and patients’) well-grounded concerns about the role EMR documentation plays in patient visits.
Someday, a new form factor for EMRs will arise — maybe augmented or virtual reality encounters, for example — which will alleviate the eyes-on-the-screen problem. Until then, I’d submit, it’s best to tackle the issue head on, not brush it off.
I’ve been kicking up some dust over on EMR and HIPAA about the awful EHR documentation that most EHR vendors produce (Full Disclosure: It’s not really the EHR vendors fault, but billing and other regulations). In response to my post, Peter Elias provided a great look at the history of medical documentation and how we got so far off track when it comes to using documentation as a clinical tool. Here’s his comment:
In the earliest days it was sparse/terse and mostly for the benefit of the clinician:
1. Document the decision and treatment. (Otitis media – amoxicillin.)
2. Document the decision, supporting evidence, and treatment. (Bulging red R TM, OM, amox.)
3. Then it became necessary to document why other decisions and treatments were not elected.
4. The SOAP note and problem oriented recording developed to encourage tracking problems over time. Still a clinical approach.
5. The medical record slowly became a legal document. If you didn’t say you examined the calf and found no evidence of DVT, it meant you hadn’t done it and were liable.
6. The medicolegal record slowly became a billing record. In order to prove how hard you worked, you needed to document two from column A, three from column B, level 37 decision making, an explicit statement of risk. This required documenting lots of negative detail. ‘Pertinent negative’ in a ROS became a laundry list of clinically irrelevant but coding-dependent negatives.
7. Add meaningful use and other audit requirements, and there is another layer of information that must be acquired and recorded.
In all this process, sadly, the note stopped being primarily a clinical tool. I fantasize about a system that allows recording of all that clinically unnecessary flotsam and jetsam, but does not require including it in a clinical note. It goes into the database and is accessible for those who want it when they want it, but it doesn’t get between me and my patients.
Reading Peter’s comments made me wonder if we’re going to start having two types of notes. A clinical note and a billing note. That’s sad to consider that EHR vendors would spend their time coding their applications around the challenge of quality documentation.
Pretty regularly, NueMD does a survey of medical practices that produces some great insights into the small practice world. This year they decided to survey medical practices about ICD-10. They’ve posted the ICD-10 survey results for those interested in really diving into the detailed survey results. They had a total of 1000 responses from primarily small and medium-sized medical practices. That sample size always gives me a little more trust in the survey.
As I looked through their ICD-10 survey results, this is the chart that really stood out to me:
The thing that attracted me to this chart first is that it highlights a number of areas where a medical practice might be concerned when it comes to ICD-10 readiness. Are you doing the right ICD-10 training and education? Have you done payer testing? Have you budgeted in any software upgrade costs that may be required to meet ICD-10? How about claims processing? Are you ready? Will you be ready by the ICD-10 deadline? These are all good questions that every organization should be asking themselves as we move towards Oct 1 (ICD-10 implementation date for those following along at home).
The second reason I love this chart is that it shows you where organizations are most concerned. I was not surprised to see that many are really afraid of how claims processing is going to go during the transition to ICD-10. What are you and your organization doing to prepare for this? It’s going to be a really big deal for many organizations and could cause them massive cash flow issues if things go bad.
The second highest was Training and Education. This is an extremely challenging one for small practices in particular. Plus, the timing is hard as well. If you train them too early, they’ll forget it come Oct 1st. If you wait to long to do the ICD-10 training, then you might not have time to train everyone that needs to be ready. I’ve seen most organizations training earlier and then doing short refresher courses or content as they get closer.
I’m planning to do another ICD-10 post soon to talk about predictions on whether ICD-10 will go forward or not. So, watch for that in the future. However, I think organizations that aren’t acting as if it’s going forward are playing a game of Russian roulette. They’re certainly braver than I’d be if I were running a healthcare organization.
Because health care has come late to the party, companies in that field have had plenty of time to see the advantages that Application Programming Interfaces (APIs) have brought to other areas of computing and commerce. BaseHealth Enterprise, which has been offering comprehensive health assessments based on a patient’s genetic information and other health factors for five years through a Software as a Service (SaaS) platform, is now joining the race to APIs. The particular pressures that led to the development of their APIs makes an interesting case study.
Although the concept of an API is somewhat technical and its details call for a bit of programming background, the concept driving API use is simple. We all use web sites and mobile apps to conduct business and interact, but an API allows two applications to talk to each other, serving as a pipe of information transfer. Thus, crucial tasks can be automated and run on a routine basis using an API. BaseHealth modestly suggests in their press release that their API “marks the first time in human history that genomic data is on-call for developers across the globe.”
Example of request for sleep apnea information
I talked last week to BaseHealth’s CEO Prakash Menon and to Hossein Fakhrai-Rad, founder and Chief Scientific Officer. They offer five basic services, all based on evaluating the genomic and phenomic (observed) data from a patient. A developer can call for such information as:
The patient’s risk for a particular common complex disease, along with risk factors that make it more likely and recommended lifestyle changes The likely effectiveness of a particular drug for a condition, given the patient’s genetic makeup Likely patient responses to various nutrients
Genomic testing is done by companies such as Illumina. Different testing services make very different judgments about the significance of various genes, but there are now evaluation sites (which perform a kind of crowdsourcing to accumulate information validating these judgments) to offer more confidence in the tests. BaseHealth accepts this data along with information about family history, lifestyle, and the patient’s environment to make useful recommendations about handling diabetes, cancer, stroke, gout, sleep apnea, and many other common conditions.
Previously, many health plans and hospitals were interested in the BaseHealth SaaS platform, but did not want to adopt a new application and UI into their existing systems because of the cost of implementation and the time it would take to train healthcare professionals on a new system. The BaseHealth API allows developers at these organizations to use specific features of BaseHealth’s comprehensive health assessment without having to overhaul their existing systems.
Furthermore, large genetic sequencing results are time-consuming and expensive to transmit, and it was wasteful to store them twice (at the provider and at BaseHealth). Some countries also prohibit the transfer of genetic data outside the country’s border for privacy reasons.
BaseHealth’s APIs therefore allow a totally different interaction model. Data can be stored by health care providers and patients, then combined by an application (usually run at the provider’s site) and submitted as a JSON data structure to the API. Only the specific information required by the API needs to be transferred. It is conceivable that apps could be developed for patient use as well. However, because BaseHealth does not offer direct-to-consumer genetic testing, they have none of the problems that 23andMe suffered.
In a field where many vendors scrutinize and limit access to APIs, it’s important to note that BaseHealth’s API is available for all to use–there is no gateway to get through, only a short registration process in which BaseHealth collects a developer’s email address. One can submit 1,000 requests each month for free-making participation easy for small providers-and then pay a small fee for further requests.
APIs hold the promise to streamline health care just as they have reduced information friction in other industries. The BaseHealth experiment illustrates why an API is useful and how it can alter the business of health care.