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Early Warnings Demonstrate an Early Advance in the Use of Analytics to Improve Health Care

Posted on May 4, 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://radar.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.

Early warning systems–such as the popular Modified Early Warning System (MEWS) used in many hospitals–form one of the first waves in the ocean of analytics we need to wash over our health care system. Eventually, health care will elegantly integrate medical device output, electronic patient records, research findings, software algorithms, and–yes, let us not forget–the clinician’s expertise in a timely intervention into patient care. Because early warning systems are more mature than many of the analytics that researchers are currently trying out, it’s useful to look at advances in early warning to see trends that can benefit the rest of health care as well.

I talked this week to Susan Niemeier, Chief Nursing Officer at CapsuleTech, a provider of medical device integration solutions. They sell (among other things) a bedside mobile clinical computer called the Neuron that collects, displays, and sends to the electronic medical record vital signs from medical devices: temperature, pulse, respiration, pulse oximetry, and so on. A recent enhancement called the Early Warning Scoring System (EWSS) adds an extra level of analytics that, according to Niemeier, can identify subtle signs of patient deterioration well before a critical event. It’s part of Capsule’s overarching aim to enable hospitals to do more with the massive amount of data generated by devices.

For more than 18 years, CapsuleTech provided bedside medical device connectivity products and services that captured patient vital signs and communicated that data to the hospital EMR. Rudimentary as this functionality may appear to people using automated systems in other industries, it was a welcome advance for nurses and doctors in hospitals. Formerly, according to Niemeier, nurses would scribble down on a scrap of paper or a napkin the vital signs they saw on the monitors. It might be a few hours before they could enter these into the record–and lots could go wrong in that time. Furthermore, the record was a simple repository, with no software observing trends or drawing conclusions.

Neuron 2 running Early Warning Scoring System

Neuron 2 running Early Warning Scoring System

So in addition to relieving the nurse of clerical work (along with likely errors that it entails), and enhancing workflow, the Neuron could make sure the record immediately reflected vital signs. Now the Neuron performs an even more important function: it can run a kind of clinical support to warn of patients whose conditions are deteriorating.

The Neuron EWSS application assigns a numerical score to each vital sign parameter. The total early warning score is then calculated on the basis of the algorithm implemented. The higher the score, the greater the likelihood of deterioration. The score is displayed on the Neuron along with actionable steps for immediate intervention. These might include more monitoring, or even calling the rapid response team right away.

The software algorithm is configured in a secure management tool accessible through a web browser and sent wirelessly to the Neuron at a scheduled time. The management tool is password protected and administered by a trained designee at the hospital, allowing for greater flexibility and complete ownership of the solution.

Naturally, the key to making this simple system effective is to choose the right algorithm for combining vital signs. The United Kingdom is out in front in this area. They developed a variety of algorithms in the late 1990s, whereas US hospitals started doing so only 5 years ago. The US cannot simply adopt the UK algorithms, though, because our care delivery and nursing model is different. Furthermore, each hospital has different patient demographics, priorities, and practices.

On the other hand, according to Niemeier, assigning different algorithms to different patients (young gun-shot victims versus elderly cardiac patients, for instance) would be impractical because mobile Neuron computers are used across the entire hospital facility. If you tune an algorithm for one patient demographic, a nurse might inadvertently use it on a different kind of patient as the computer moves from unit to unit. Better, then, to create a single algorithm that does its best to reflect the average patient. The algorithm should use vital signs and observations that are consistently collected, not vitals that are intermittently measured and documented.

Furthermore, algorithms can be tuned over time. Not only do patient populations evolve, but hospitals can learn from the data they collect. CapsuleTech advises a retrospective chart review of rapid response events prior to selecting an algorithm. What vital signs did the patient have during the eight hours before the urgent event? Retrospectively apply the EWSS to the vital signs to determine the right algorithm and trends in that data to recognize deterioration earlier.

Without help such as the Early Warning Scoring System, rapid response teams have to be called when a clear crisis emerges or when a nurse’s intuition suggests they are needed. Now the nurse can check his intuition against the number generated by the system.

I think clinicians are open to the value of analytics in early warning systems because they dramatically heighten chances for avoiding disaster (and the resulting expense). The successes in early warning systems give us a glimpse of what data can do for more mundane aspects of health care as well. Naturally, effective use of data takes a lot more research: we need to know the best ways to collect the data, what standards allow us to aggregate it, and ultimately what the data can tell us. Advances in this research, along with rich new data sources, can put information at the center of medicine.

Telehealth, or ‘How to Ditch the Waiting Room’

Posted on February 13, 2015 I Written By

The following is a guest blog post by Ryan Nelson, Director of Business Development for Medical Web Experts.

Navigating the doctor’s office for a non-emergency can feel like getting lost in a quagmire of lengthy routines. For those who choose to forego the experience for as long as possible, haphazardly browsing WebMD in the middle of the night is no better. This could all change soon.

Telemedicine is on the rise as health insurers and employers have become more willing to pay for online video consultations in recent years. Convenience (imagine not having to leave the comfort of your home for every service!) and positive health outcomes – not to mention significant cost savings for both employers and patients – are propelling online video consultations to the forefront of healthcare strategies.

Convenience
People don’t like driving far, and they don’t like spending 45 minutes in a waiting room only to be discharged in under 15. The average wait time for a doctor’s appointment is 20 days in the US. This is more than enough time to deter patients from booking appointments for conditions that could be minor. Doctors usually don’t get reimbursed for time spent taking phone calls, so they often nix the medium altogether. Virtual doctor visits can fulfill patients’ need for instantaneous advice, closing a potentially dangerous communication gap while opening a new business opportunity for healthcare professionals.

A recent Harris Poll survey commissioned by Amwell found that around 40% of consumers would opt for video appointments for both antibiotics and birth control prescriptions, while at least 70% would rather have an online video visit to obtain a prescription than travel to their doctor’s office. Telehealth also offers a good solution for patients with mobility issues or chronic conditions, and it gives patients and doctors in rural or remote communities more options for receiving and dispensing care.

Health Outcomes
Biomed Central’s systematic review of telehealth service studies revealed that health outcomes for telehealth and in-person appointments are usually similar. About one-third of studies showed improved outcomes and only two indicated that telehealth was less effective. One way that online video appointments can improve health outcomes for the general population is to filter out minor health concerns and free up ER staff to deal with more serious ailments in-house. Additionally, video consultations can make it easier for physicians to track the recovery of discharged patients and to monitor patient adherence in a time-sensitive manner.

Cost Savings
The Amwell survey revealed that 64% of patients are willing to attend virtual appointments, challenging the dated assumption that in-person interactions tend to be perceived as a better experience. Contributing to this popularity is the fact that virtual appointments cost much less than an ER visit and are cheaper than an urgent care center or most face-to-face consults, generally figuring in around $40 to $50.

Biomed Central also found that out of 36 studies, nearly two-thirds showed cost savings for employers and patients. Meanwhile, Towers Watson predicted that the number of employers offering telemedicine will increase by 68% in 2015, which would result in $6B in employer savings.

Consumer Concerns
Consumers are concerned about how doctors can thoroughly examine patients through video, according to Amwell. However, the proliferation of self-monitoring mobile devices that can be used in conjunction with video consultations suggests that doctors may be able to get much of the information they need online. Besides, it can be argued that during most medical appointments a doctor doesn’t have much time to perform a comprehensive examination or truly get to know a patient.

Amwell subjects also questioned how a patient can be certain that he or she is speaking to a real doctor; however, this can easily be addressed by medical web platforms that thoroughly screen physicians and can thus provide adequate proof of their qualifications.

Digital Relationships
Research has shown that online video communication improves patient satisfaction and increases efficiency and access to healthcare for all demographics, at all times. While the medium appeals to people across all age groups, it especially appeals to younger, tech-savvy patients. This demographic tends to prefer instantaneous communication for non-emergencies and is generally comfortable communicating despite physical distance.

Consumers already use technology to communicate with their friends and families. Finally, doctors – another one of every person’s most intimate relationships – can join the ranks.

References:
Amwell
Biomed
Towers Watson

Apervita Creates Health Analytics for the Millions

Posted on January 9, 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://radar.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.

Health officials are constantly talking up the importance of clinical decision support, more popularly known now as evidence-based medicine. We’re owning up to the awkward little fact–which really should embarrass nobody–that most doctors lack expertise on many of the conditions they encounter and can’t read the thousands of relevant studies published each year. New heuristics are developed all the time for things such as predicting cardiac arrest or preventing readmissions after surgery. But most never make their way into the clinic.

Let’s look at what has to happen before doctors and patients can benefit from a discovery:

  1. The researcher has to write a paper with enough detail to create a working program from the heuristic, and has to publish the paper, probably in an obscure journal.

  2. A clinician or administrator has to find the article and line up staff to write and thoroughly test a program.

  3. If the program is to be used outside the hospital where it was created, it has to be disseminated. The hospital is unlikely to have an organization set up to package and market the program. Even if it is simply put out for free use, other institutions have to learn about it and compile it to work on their systems, in order for it to spread widely. Neither the researcher nor the hospital is likely to be compensated for the development of the program.

  4. The program has to be integrated into the doctor’s workflow, by being put on a menu or generating an alert.

Evidence-based medicine, therefore, is failing to tap a lot of resources that could save lives. A commonly cited observation is that research findings take 17 years to go into widespread practice. That’s 17 years of unnecessary and costly suffering.

I have often advocated for better integration of analytics into everyday medical practice, and I found a company called Apervita (originally named Pervasive Health) that jumps off in the right direction. Apervita, which announced a Series A round of funding on January 7, also has potential users outside of clinical settings. Pharma companies can use it to track adverse drug events, while payers can use it to predict fraud and risks to patients. There is not much public health data in the platform yet, but they’re working on it. For instance, Leapfrog group has published hospital safety info through their platform, and Diameter Health provides an all-cause 30-day readmissions prediction for all non-maternal, non-pediatric hospitalizations.

Here’s how the sequence of events I laid out before would go using Apervita:

  1. The researcher implements her algorithm in Python, chosen because Python is easy for non-programmers to learn and is consequently one of the most popular programming languages, particularly in the sciences. Apervita adds functions to Python to make it easy, such as RangeCompute or tables to let you compute with coefficients, and presents these through an IDE.

  2. The researcher creates an analytic on the Apervita platform that describes and publishes the analytic, along with payment terms. Thus, the researcher derives some income from the research and has more motivation to offer the analytic publicly. Conversely, the provider pays only for usage of the analytic, and does not have to license or implement a new software package.

  3. Clinicians search for relevant analytics and upload data to generate reports at a patient or population level. Data in popular formats such as Excel or comma-separated value (CSV) files can be uploaded manually, while programmers can automate data exchange through a RESTful web service, which is currently the most popular way of exchanging data between cooperating programs. Rick Halton, co-founder and Chief Marketing Officer of Apervita, said they are working on support for HL7’s CCD, and are interested in Blue Button+ button, although they are not ready yet to support it.

  4. Clinicians can also make the results easy to consume through personalized dashboards (web pages showing visualizations and current information) or by triggering alerts. A typical dashboard for a hospital administrator might show a graphical thermometer indicating safety rankings at the hospital, along with numbers indicating safety grades. Each department or user could create a dashboard showing exactly what a clinician cares about at the moment–a patient assessment during an admission, or statistics needed for surgical pre-op, for instance.

  5. Apervita builds in version control, and can automatically update user sites with corrections or new versions.

I got a demo of Apervita and found the administration pretty complex, but this seems to be a result of its focus on security and the many options it offers large enterprises to break staff into groups or teams. The bottom line is that Apervita compresses the difficult processes required to turn research into practice and offers them as steps performed through a Web interface or easy programming. Apervita claims to have shown that one intern can create as many as 50 health analytics in one week on their platform, working just from the articles in journals and web resources.

The platform encrypts web requests and is HIPAA-compliant. It can be displayed off-platform, and has been integrated with at least one EHR (OpenMRS).

Always attuned to the technical difficulties of data use, I asked Halton how the users of Apervita analytics could make sure their data formats and types match the formats and types defined by the people who created the analytics. Halton said that the key was the recognition of different ontolgies, and the ability to translate between them using easy-to-create “codesets.”

An ontology is, in general, a way of representing data and the relationships between pieces of data. SNOMED and ICD are examples of common ontologies in health care. An even simpler ontology might simply be a statement that units of a particular data field are measured in milliliters. Whether simple or complex, standard or custom-built, the ontology is specified by the creator of an analytic. If the user has data in a different ontology, a codeset can translate between the two.

As an example of Apervita’s use, a forward prediction algorithm developed by Dr. Dana Edelson and others from the University of Chicago Medical Center can predict cardiac arrests better than the commonly used VitalPAC Early Warning Score (ViEWS) or Modified Early Warning Score (MEWS). Developed from a dataset of over 250,000 patient admissions across five hospitals, “eCART” (electronic Cardiac Arrest Triage) can identify high-risk hospital ward patients and improve ICU triage decisions, often as much as 48 hours in advance.

The new funding will allow Apervita to make their interface even easier for end-users, and to solicit algorithms from leading researchers such as the Mayo Clinic.

Halton heralds Apervita as a “community” for health care analytics for authors and providers. Not only can the creators of analytics share them, but providers can create dashboards or other tools of value to a wide range of colleagues, and share them. I believe that tools like Apervita can bridge the gap between the rare well-funded health clinic with the resources to develop tools, and the thousands of scattered institutions struggling to get the information that will provide better care.

Consumers Are Still Held Back From Making Rational Health Decisions

Posted on November 25, 2014 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://radar.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.

Price and quality of care–those are what we’d like to know when we need a medical procedure. But a perusal of a recent report from the Government Accountability Office reminded me that both price and quality information are hard to get nowadays.

This has to make us all a little leery about trends in health reform. Governments, insurers, and employers want us to get choosy about where we have our procedures. They justify rises in copays and deductibles by saying, “You patients should start to take responsibility for the costs of your own health care.”

Yeah, as responsible as a person looking for his car keys in the dark. Let’s start with prices, which in many countries are uniform and are posted on the clinic wall.

Sites such as Clear Health Costs and Castlight Health prove what we long knew anecdotally: charges in the US vary vertiginously among different institutions. Anyone who had missed that fact would have been enlightened by Steven Brill’s 2013 Time Magazine article.

But aspirations become difficult when we get down to the issue at hand–choosing a provider. That’s because US insurance and reimbursement systems are also convoluted. We don’t know whether a hospital will charge our insurer their official price, or how much the insurer will cover. It might feel righteous to punish a provider with high posted prices (or prices reported by other consumers), but most patients have a different goal: to keep as much of their own money as they can.

We can gauge the depth of the cost problem from one narrow suggestion made in the GAO report that yet could help a lot of health consumers: the suggestion that Centers for Medicare & Medicaid Services (CMS) publish out-of-pocket expenditures for Medicare recipients as well as raw costs of procedures (page 31). Even this is far from simple. HHS pointed out that 90% of Medicare patients have supplemental overage that reduces their out-of-pocket expenditures (page 43). Tracking all the ancillary fees is also a formidable job.

Castlight Health is out in front when it comes to measuring the real impact of charges on consumer. They achieve great precision by hooking up with employers. Thus, they know the insurer and the precise employer plan that covers each individual visiting their site, and can take deductibles, exclusions, and caps into account when calculating the cost of a procedure. A recent study found that Castlight users enjoyed lower costs, especially for labs and imaging. Some nationwide system built around standards for reporting these things could unpack the cost conumdrum for all patients.

Let’s turn to quality. As one might expect, it’s always a slippery concept. The GAO report pointed out that quality may be measured in different ways by different providers (page 26). A recently begun program releases Medicare data on mortality and readmissions, but it hasn’t been turned into usable consumer information yet (pages 27-28). Two more observations from the report:

  • “…with the exception of Hospital Compare, none of CMS’s transparency tools currently provide information on patient-reported outcomes, which have been shown to be particularly relevant to consumers considering common elective medical procedures, including hip and knee replacements.” (Page 21)

  • “CMS’s consumer testing has focused on assessing the ability of consumers to interpret measures developed for use by clinicians, rather than to develop or select measures that specifically address consumer needs.” (Page 25)

Some price-check sites simply don’t try to measure quality. A highly publicized crowdsourcing effort by California radio station KQED, based on the Clear Health Costs service, admitted that quality measures were not available but excused themselves by citing the well-known lack of correlation between price and quality.

Price and quality may not be related, but that doesn’t relieve consumers of concerns over quality. Can you really exchange Mount Sinai Hospital in New York for Daddy-o’s Fix-You-Up Clinic based on price alone? Without robust and reliable quality data, people will continue choosing the historically respected hospitals with the best marketing and PR departments–and the highest prices.

A recent series on health care costs concludes by admonishing consumers to “get in the game and start to push back.” The article laments the passivity of consumers in seeking low-cost treatment, but fails to cite the towering barriers that stand in the way.

The impasse we’ve reached on consumer choice, driven by lack of data, reflects similar problems with analytics throughout the health care field. For instance, I recently reported on how hard a time researchers have obtaining and making use of patient data. Luckily, the GAO report cites several HHS efforts to enhance their current data on price and quality. Ultimately, of course, what we need is a more rational reimbursement system, not a gleaming set of computerized tools to make the current system more transparent. Let’s start by being honest about what we’re asking health consumers to achieve.

Treating a Healthy Patient

Posted on November 21, 2014 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I first coined the concept of what I call treating a healthy patient back in 2011. I’ve always loved the concept of a doctor actually treating someone who thinks and feels completely healthy. The challenge is that this type of relationship is very different than what we have in our current health system today.

While our current model is very different, I’m hearing more and more things that get me back to healthcare treating an otherwise healthy patient. Although, someone recently pointed out to me that we’re not really treating a healthy patient, because we’re all sick. We just each have different degrees of sickness. It’s a fine point, but I still argue we’re “healthy” because we feel “healthy.”

This analysis points out one layer of change that I see happening in healthcare. This change is being able to detect and predict sickness. Yes, that still means a doctor is treating a sickness. However, I see a wave of new sensors, genetics, and other technology that’s going to absolutely change what we define as “sick.”

This is a massive change and one that I think is very good. I recently read an article by Joseph Kvedar which commented that we’re very likely to seek medical help when we break our arm, because the pain is a powerful motivating factor to get some help. Can this new wave of sensors and technology help us know the “pain” our bodies are suffering through and thus inspire us to seek medical attention? I think they will do just that.

The problem is that our current health system isn’t ready to receive a patient like this. Doctors are going to have to continue to evolve in what they consider a “disease” and the treatment they provide. Plus, we’ll likely have to include many other professionals in the treatment of patients. Do we really want our highly paid doctors training on exercise and nutrition when they’ve had almost no training in medical school on the subjects? Of course, not. We want the dietitian doing this. We’ll need to go towards a more team based approach to care.

I’ve regularly said, “Treating a healthy patient is more akin to social work than it is medicine.” Our health system is going to have to take this into consideration and change accordingly.

Treating a healthy patient won’t solve all our healthcare problems. In fact, I’ve wondered if in some ways treating a healthy patient isn’t just shifting the costs as opposed to lowering the costs. Regardless of the cost impact, this is where I see healthcare heading. Yes, we’ll still need many doctors to do important procedures. Just because you detect possible heart issues doesn’t mean that patient won’t eventually need a heart bypass surgery some day. In fact, a whole new set of medical procedures will likely be created that treat possible heart issues before they become straight up heart issues.

What other ways do you see the system moving towards or away from “treating healthy patients”?

Which Comes First in Accountable Care: Data or Patients?

Posted on September 30, 2014 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://radar.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.

The headlines are stark and accusatory. “ACOs’ health IT capabilities remain rudimentary.” “ACOs held back by poor interoperability.” But a recent 19-page survey released by the eHealth Initiative tells two stories about Accountable Care Organizations–and I find the story about interoperability less compelling than another one that focuses on patient empowerment.
Read more..

Are Limited Networks Necessary to Reduce Health Care Costs?

Posted on September 10, 2014 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://radar.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.

Among the dirty words most hated by health care consumers–such as “capitation” and “insufficient medical necessity”–a special anxiety infuses the term “out-of-network.” Everybody harbors the fear that the world-famous specialist who can provide a miracle cure for a rare disease he or she may unexpectedly suffer from will be unavailable due to insurance limitations. So it’s worth asking whether limited networks save money, and whether they improve or degrade health care.
Read more..

EHRs Don’t Make Errors, People Do

Posted on July 31, 2014 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

An intriguing blog title, I know. I saw it on Bill Crounse, MD’s blog post and couldn’t resist extending the discussion. This is a really challenging topic and so it’s definitely worth of discussion.

On the one hand it’s clear to me that EHR software isn’t perfect. However, paper charts weren’t perfect either. On the other hand, people aren’t perfect either. Unfortunately, we don’t want to admit our imperfection and our society has gotten to the point that imperfections are unacceptable.

In the blog post mentioned above, Dr. Crounse offers the following suggestions and I’ll add my own commentary for each:

Involve the Patient Right from the Start – I’m hopeful that some of the companies working on this problem will get widespread adoption. The patient could definitely be more involved in entering their patient data before the visit even happens and thus relieve the burden on the clinician. This is a challenging problem to solve though when you consider the vast array of physician preferences.

Ease the Documentation Burden on Clinicians – This is mostly a knock on our current billing system. If we make the switch to value based reimbursement can we ease the documentation burden on clinicians? That’s worthy of its own post and some deeper thought. Sadly, I think in the short term it likely means more documentation burden for clinicians. I don’t see this happening soon, but it’s a noble goal.

Prohibit Templates, Cut and Paste – I generally disagree with this one. Ironically, the title of the post illustrates my issue with it, “Electronic Health Record solutions don’t make errors, people do.” It’s not templates and cut and paste that’s the problem as much as it is rushed physicians who don’t use it appropriately. I think one word describes most of the issues: laziness. I know. When I use a template for my blog posts or email blasts, I get lazy on them sometimes too. Fortunately, my blog posts or emails don’t have people’s lives hanging on them. So, maybe Dr. Crounse has a point. It’s just too easy to screw up templates and copy/paste.

Share Information with Patients – I’ve long been a proponent of the patient being aware of the information in the paper chart. I know that many doctors fear this. Usually they reference the fear that patients won’t understand the information that’s in the chart. I’ve just not seen this to be the case in practice and the benefits of the patient being able to be involved in their chart is so much more valuable than any perceived risk. The harder part is that I haven’t seen any system which creates a simple way for the patient to update/correct/verify information in a chart. Access is a great step forward, but the next steps is to empower the patient to assist in the patient chart quality control process.

As long as we have imperfect humans using imperfect EHR software, errors are going to happen. However, we can do better than we’re doing today. I like the ideas that Dr. Crounse suggested. I’d love to hear any ideas you have as well.

athenahealth Partners With Quality Group To Research EMR Patient Safety

Posted on November 15, 2013 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

While it’s known that EMRs have been involved with, and probably responsible for, patient harm and even death, research is incomplete and sketchy on what risks are the most pressing and how to avoid them. Plus, we’re always balancing these risks with the potential benefits of EMR as well.

One recent study by the Pennsylvania Patient Safety Authority concluded that EMR default settings for medications caused adverse events in more than 3 percent of cases reviewed by the organization.

But that’s just one study, which can only do so much to help on its own. To get a better grip on such issues, EMR and practice management vendor athenahealth has partnered with Patient Safety Organization Quantros to examine the impact that EMRs are having on patient care. The research project is being funded by athenahealth, according to  a piece in Medical  Practice Insider.

athenahealth is offering its national network of about 47,000 providers free access to Quantros’ Safety Event Manager reporting tool, allowing athena’s EMR clients to submit patient safety data directly to the Quantros Patient Safety Center. Delivering the safety data through a PSO like Quantros insulates providers from liability by offering discovery protections when the practices report and analyze a potential issue, Medical  Practice Insider reports.

As one might expect, athena is mounting the experiment to find out when use of its EMR might have contributed to a  potential adverse event, such as, for example, when the EMR fails to warn a physician that a prescribed drug would interact with a drug the patient is already taking.

The bottom line, for athena, is to analyze the data for patient safety trends, and use it directly to improve its technology, said Tarah Hirschey, athena’s senior manager of patient safety, to Medical  Practice Insider.

100% Interoperability, Quantified Self Data, and Data Liquidity – #HITsm Chat Highlights

Posted on March 30, 2013 I Written By

Katie Clark is originally from Colorado and currently lives in Utah with her husband and son. She writes primarily for Smart Phone Health Care, but contributes to several Health Care Scene blogs, including EMR Thoughts, EMR and EHR, and EMR and HIPAA. She enjoys learning about Health IT and mHealth, and finding ways to improve her own health along the way.

Topic 1: Do you think the healthcare system WANTS 100% interoperability & data liquidity? Why/why not?

 

Topic 2: As consumer, what are YOUR fears about your health data being shared across providers/payers/government?

 

Topic 3: What do you think payers will do with #quantifiedself data if integrated into EHR? Actuarial/underwriting?

 

Topic 4: Could there be a correlation between your fear of data liquidity and your health?

 

Topic 5: What could assuage your fears? Education? Legislation? Regulation? Healthcare system withdrawal?