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The Current State Of “Big Data” In Healthcare – Health Care CXO Scene

Posted on November 2, 2015 I Written By

David is a global digital healthcare leader that is focusing on the next era of healthcare IT.  Most recently David served as the CIO at an academic medical center where he was responsible for all technology related to the three missions of education, research and patient care. David has worked for various healthcare providers ranging from academic medical centers, non-profit, and the for-profit sectors. Subscribe to David's latest CXO Scene posts here.

Editor’s Note: A big welcome to David Chou, the newest member of the Healthcare Scene family of bloggers. David has a great background as a hospital CIO and will bring a wealth of knowledge to Hospital EMR and EHR readers. We’re calling David’s series of blog posts the Healthcare CXO Scene. You can receive the CXO Scene blogs by email as well. Welcome David!

Healthcare is finally evolving towards utilizing data in our decision-making.  The landscape has changed dramatically with the adoption of Electronic Medical Record across the nation. Healthcare use to be a predominately paper based vertical and there are still lots of areas where it is dominated by paper. The fax is also still alive as a communication channel, but the industry has transformed dramatically in the last few years.

According to the Office Of The National Coordinator in 2013, nearly six in ten (59%) hospitals had adopted at least a Basic EHR system. This represents an increase of 34% from 2012 to 2013 and a five-fold increase since 2008. I am sure that percentage is even higher in 2015 in our journey towards an electronic world.

The workflow for the clinician and physician documentation does take a little longer now that they have to type instead of write their notes, but the advantages of having discrete data elements to run analytics will transform the decision making of every organization. If you Google the definition of “big data” the consensus definition is the wealth of structured, semi-structured and unstructured data that has the potential to be mined for information.

Unfortunately the healthcare vertical is still playing catch up and the majority of the organizations still only have Electronic Medical Record (EMR) data being used for decision-making. The healthcare vertical use to be similar to the airline industry where the key to success was keeping the hospital beds occupied similar to how the airline industry wanted to keep every seat on the airplane filled. The new model of care is figuring out a mechanism to keep patients out of the hospital beds and focus on keeping them healthy through preventative measures. We have to do all of this while figuring out the right financial model to be profitable.

As we move down the journey where we transition from a fee for service payment model to a value based payment model it is critical for every organization to transform their business process. Analytics will be key in making that change. Now let’s focus on the 2 key challenges that will force healthcare providers to focus on data to drive their decisions impacting their operations internally and externally.

Challenge #1: Healthcare reimbursements from Medicare and Medicaid have reduced year after year

This has a huge financial impact on health care since the Medicare expenditures have been growing as the baby boomer population ages. There has also been a steady increase of Medicaid expenditures, so the trend of lower reimbursements for taking care of a growing population will be what lies ahead for us in health care. Effective, quality delivery of care while reducing waste will be the main driver of success in the future.

Healthcare providers must understand the cost of delivering care down to the unit level. You will be surprised by the variation of cost for various procedures. The same procedure cost can vary by as much as 15-25% based on the products used. So one of the key elements of cost containment is standardization. As we transition to a value based payment model there will also be value based contracts which will be structured towards a shared savings model. The contractual terms will vary but the general theme will be to incentivize the providers to reduce cost for providing quality care to a population by offering a percentage of the net savings. We are seeing this trend in the Medicare shared saving program and leveraging data analytics will be the key-driving tool for this to be successful.

Challenge #2: The Move Towards Personalized Care

Consumers/patients have different expectations now. We are living in an on-demand personalized world where every industry vertical is moving towards a predictive environment including healthcare. The ideal scenario would be to consume data from the social platforms, wearables/sensors, mobile, public data, and other sources so that we can really understand in real time the current state of the consumer/patient.

Let’s assume the scenario of a digital consumer who is currently a diabetic patient that has been prescribed to be on a low calorie diet. The patient wears a fitbit and also has their smartphone app that tracks her heart rate. The heart rate is a bit higher than normal and the patient feels a little bit off. This wearable and mobile app is integrated with a central monitoring system at the hospital and an alarm triggers a clinician who checks the patient profile and history and takes the proactive measure of making a video call to the patient.

The patient answers the video call with the clinician and they have a video interaction where the clinician can see the facial color of the patient and asks a few questions. Fortunately the patient finished an intense workout about a hour ago so things are fine with the irregular heart rate at the moment and this video interaction also alleviates any anxiety for the patient. It is about 7pm so the patient decides to get something to eat and he is craving a burger so he pulls in to the drive through. The patient has his GPS turned on from his smartphone and also posts on Facebook that he is at a fast food chain’s drive through. This data element is picked up by the hospital’s CRM app and then an automated text is sent to the patient reminding him of the low calorie diet and makes a few recommendation from the menu. The patient can now make an inform decision and instead of ordering a burger he orders a grilled chicken sandwich.

The technology that I have described is already in place and it is similar to the retail sector when you walk in to the store and they already know your behavior. There is a trigger to create an action which hopefully equates to a sale.

Healthcare must move towards this culture of living in an on demand world where we can predict or persuade a behavior by the patient. The challenge that I see is that the majority of healthcare providers are still focused on their internal operations leveraging EMR data and we have not focused on the digital consumer yet. There are a lot of great work being put together by enterprise vendors and healthcare providers, but as we move down the journey of managing population health we can really learn from the other verticals and how they leverage the big data technology to improve consumer/patient engagement. All of this will ultimately lead to a healthier population.

If you’d like to receive future health care C-Level executive posts by David in your inbox, you can subscribe to future Health Care CXO Scene posts here.

Another Giant In Play: 3M Looking At “Strategic Alternatives” For HIS Unit

Posted on September 14, 2015 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

Given the staggering number of EMR launches that took place in the wake of the Meaningful Use kickoff, mergers, sell-offs and business failures were quite predictable. Despite the feds’ doling out $30B in incentive dollars, even that wasn’t enough to keep hundreds of EMR entrants afloat.

It hasn’t been as clear what would happen to large vendors with HIT interests, given that they had enough capital to ride more than one wave of provider adoption. The field has just begun to shake out, with only a small handful of major transactions taking place. Recent plays by large tech players include Cerner’s $1.3B acquisition of Siemens Health Services, which included the Soarian EMR. There’s also ADP’s sale of EMR solution AdvancedMD to Marlin Equity Partners after previously acquiring e-MDs. Not to mention Greenway and Vitera Healthcare Solutions joining forces and Pri-Med acquiring Amazing Charts.

Another major move was announced this April at HIMSS 15, when GE Healthcare announced that it was phasing out its Centricity Enterprise product. According to news reports, the Enterprise product only generated 5% of the Healthcare division’s EMR revenue. I could keep going, but you get the point.

Now, 3M has joined the fray, announcing this week that it was “exploring strategic alternatives” for its HIS business, including spinning off or selling the unit.  (It’s also considering keeping its HIS business on board and investing in its future.)  The company, which has signed Goldman, Sachs & Co. as strategic advisor and investment banker, says that it will probably announce what direction it will head in by the end of the first quarter of next year.

On the surface, 3M Health Information Systems looks like a very solid business. The HIS unit, which is focused on computer-assisted coding, clinical documentation improvement, performance monitoring, quality outcomes reporting and terminology management, reportedly works with more than 5,000 hospitals, plus government and commercial payers. According to 3M, the HIS business generated trailing 12-month revenues of about $730M, and has sustained 10%+ compounded annual growth for 10 years.

That being said, it’s hard to say what the fallout from the ICD-10 switchover will be, and it’s not unreasonable for 3M to consider whether it wants to compete in the post-switchover world. After all, while the HIS unit seems to be quite healthy, it’s certainly faces stiff competition from several directions, including EMRs with integrated billing and coding technology. Also, the company may be saddled with outdated legacy infrastructure, which makes it hard to keep up in this new era of revenue cycle management.

By the end of the first quarter of 2016, 3M will have had a chance to see how its customers are faring post-ICD-10, and how its customers needs are shifting. 3M will also find out whether other HIS players with (presumably) newer technology in place are interested in doing a rollup with its business.

Truthfully, if 3M doesn’t think it can benefit from investing in the HIS unit, I’m not sure who else would benefit from doing so. In fact, I’d argue that 3M is undermining its chances at a deal by waffling over whether it plans to invest or divest; as I see it, this implies that the HIS unit will be on life support without a major cash infusion, which is not something I’d find attractive as an investor.  If nothing else I’d want to buy the unit at a firesale price! But I guess we’ll have to wait until March 2016 to see what happens.

Under the Hood of Medical Devices

Posted on September 11, 2015 I Written By

The following is a guest blog post by Kevin Phillips, Vice President – Marketing and Product Management at CapsuleTech.
Value of Medical Device Data

When it comes to medical devices, most people think of patient monitoring and physiologic data such as HR, SPO2, respiration rate waveforms and physiologic alarms. But there’s a lot more “under the hood” of a device – a lot more than just physiologic data that, when applied in new ways, can contribute to patient safety efforts and help with operational efficiencies.

Under the hood are three types of data.  The first, and most often understood and used, is patient data that provides information on the physiologic status of the patient; a snapshot, if you will, of a patient’s condition at a given moment in time. The second type of data is treatment details.  These details provide a comprehensive view of treatments being administered to a patient, and include the names of drugs or anesthetic agents, drug concentration, the volume to be infused, or volume of air being delivered via a ventilator.  The third type of data is about the devices themselves. This information includes not only modes of operation, technical alarms, and battery level, but also data, such as firmware versions and unique device identifiers, that is useful to the clinical engineers responsible for maintaining these devices.

Of course, all of this data is meaningless without context.  This “contextual device data” can be added by external systems such as an EMR or by Capsule’s SmartLinx Medical Device Information System®. We define context as key information for each device: how the device is being used; where it is located; to which patient it is connected; and the identity of the primary clinician responsible for this patient. We also want to know information about the device itself including its unique device identifier, synchronized time (e.g. measurement time, device time, and NTP server time). Last, of course, are the clinical observations of the patient.

Today, only a fraction of this data…maybe 10%…is being used by a hospital; what is being used is typically only that data specified by the hospital by its EMR.  And while not all of the remaining 90% of the data is useable in some cases, there is a fair amount of significant value if mined and delivered to the appropriate system or user when it is needed.  Some examples include:

  • Alarm Management Systems – Well-documented patient safety risks posed by the failure to adequately address medical device alarms management by publications such as ECRI has led the Joint Commission to create a National Patient Safety Goal. This goal requires all hospitals to have a policy in place to manage alarms appropriately by 1/01/2016.  This has driven a demand for medical device data like near real-time notification of high priority physiologic and technical alarms from each device.  The art to these data integrations is close collaboration to deliver the proper alarms so not to overwhelm the clinician with nuisances (low priority alarms).
  • Device utilization – While solutions exist to help identify the location of expensive, high-maintenance devices, determining which devices are in use is difficult. Providing timely and appropriate device data to biomedical teams can ensure optimal device management, use and health, easing patient throughput and contributing to patient safety and care.
  • Clinical Decision Support Systems – Whether hospitals have created their own algorithms or purchased a turn-key solution, CDSS’s require high frequency physiologic medical device measurements to properly power their specific algorithms to enable them to identity patients at risk of sepsis or deterioration.
  • Patient Surveillance Applications – Automated patient surveillance helps clinicians to remotely wade through vast information stores to quickly discern data of the greatest value. With the addition of real-time device data, patient surveillance applications can better identify data clusters and trends consistent with patient deterioration and specific disease conditions, prompting clinical intervention.
  • Asset Management – While asset-tracking solutions can help identify the current location of devices, determining which devices are in use or underutilized is difficult. Devices offer a range of built-in operational checks, or support remote monitoring to ensure device readiness and status of any required supplies. The availability of this data to biomedical teams will ensure optimal device management and health, easing patient throughput and boosting patient safety and care.

So what’s under the hood of all of your medical devices?  Probably a whole lot more that you ever imagined that can be of immense value throughout your hospital. Why don’t you take a look today to see what value can be derived.

About Kevin Phillips
Kevin Phillips is the Vice President – Marketing and Product Management at CapsuleTech with over 10 years of experience in various roles within the healthcare, medical device and diagnostic industries. His career has been focused on new product development, product marketing, market analysis, strategic alliances, corporate operations, and sales. Prior to joining Capsule, Mr. Phillips held positions at TransMedics and PathoGenetix (formerly US Genomics). His career has been focused on new product development, product marketing, market analysis, strategic alliances, corporate operations, and sales.

EMRs Must Support Hospital Outcomes Reporting

Posted on August 25, 2015 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

Should a hospital be paid if it doesn’t make its outcomes statistics public? Pediatric heart surgeon Dr. Jeffrey Jacobs says “no.” Jacobs, who chairs the Society of Thoracic Surgeons National Database workforce, recently told CNN that he believes reimbursement should be tied to whether a hospital shares data transparently. “We believe in the right of patients and families to know these outcomes,” said Jacobs, who is with the Johns Hopkins All Children’s Heart Institute in St. Petersburg, FL.

Jacobs’ views might be on the extreme side of the industry spectrum, but they’re growing more common. In today’s healthcare industry, which pushes patients to be smart shoppers, hospitals are coming under increasing pressure to share some form of outcomes data with the public.

I’ve argued elsewhere that in most cases, most hospital report cards and ratings are unlikely to help your average consumer, as they don’t offer much context how the data was compiled and why those criteria mattered. But this problem should be righting itself. Given that most hospitals have spent millions on EMR technology, you’d think that they’d finally be ready to produce say, risk-adjusted mortality, error rates and readmissions data patients can actually use.

Today, EMRs are focused on collecting and managing clinical data, not providing context on that data, but this can be changed. Hospitals can leverage EMRs to create fair, risk-adjusted outcomes reports, at least if they have modules that filter for key data points and connect them with non-EMR-based criteria such as a physician’s experience and training.

While this kind of functionality isn’t at the top of hospitals’ must-buy list, they’re likely to end up demanding that EMRs offer such options in the future. I foresee a time when outcomes reporting will be a standard feature of EMRs, even if that means mashing up clinical data with outside sources. EMRs will need to interpret and process information sources ranging from credentialing databases and claims to physician CVs alongside acuity modifiers.

I know that what I’m suggesting isn’t trivial. Mixing non-clinical data with clinical records would require not only new EMR technology, but systems for classifying non-clinical data in a machine-readable and parseable format. Creating a classification scheme for this outside data is no joke, and at first there will probably be intermittent scandals when EMR-generated outcomes reports don’t tell the real story.

Still, in a world that increasingly demands quality data from providers, it’s hard to argue that you can share data with everyone but the patients you’re treating. Patients deserve decision support too.

It’s more than time for hospitals to stop hiding behind arguments that interpreting outcomes data is too hard for consumers and start providing accurate outcomes data. With a multi-million-dollar tool under their roof designed to record every time a doctor sneezes, analyzing their performance doesn’t take magic powers, though it may shake things up among the medical staff.  Bottom line, there’s less excuse than ever not to be transparent with outcomes. And if that takes adding new functionality to EMRs, well, it’s time to do that.

EHR Alerts, Top 10 Health IT Topics, Gesture Based EHR, and Adverse Events

Posted on December 18, 2014 I Written By

John Lynn is the Founder of the 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 and 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 thought it might be valuable to highlight a few interesting tweets I’ve seen recently. Some of them come from the other Healthcare Scene blogs, but I think you’ll find interesting.

Have alerts helped your organization? Alert fatigue is a very real thing, but when calibrated effectively, I’ve seen them really benefit an organization.

This is a fun list of healthcare topics. Do you see any topics that should be added to the list?

We’ve heard about gesture based EHR many times before. Mostly in the surgery room and mostly as demonstration projects. I don’t think this will really go huge and mainstream in healthcare, but could likely get some pickup for very targeted use cases.

Carl does a really great job in this article talking about Adverse Events and the legislation that’s proposed around EHR adverse events. This is a really important topic that doesn’t get nearly enough attention.

ACOs Stuck In Limbo In Trying To Build HIT Infrastructure

Posted on September 26, 2014 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

Though they try to present themselves differently, ACOs are paper tigers. While they may be bound together by the toughest contracts an army of lawyers can devise, they really aren’t integrated in a meaningful way.

After all, the hospitals and medical groups that make up the ACO still have their own leadership, they don’t generally hold assets in common other than funds to support the ACO’s operations, and they’re definitely not in a great position to integrate technically.

So it comes as no surprise that a recent study has found that ACOs are having a hard time with interoperability and rolling out advanced health IT functions.

The study, a joint effort by Premier and the eHealth Initative, surveyed 62 ACOs. It found that 86% had an EMR, 74% had a disease registry, 58% had a clinical decision support system, and 28% had the ability to build a master patient index.

Adding advanced IT functions is prohibitively difficult for many, researchers said. Of the group, 100% said accessing external data was difficult, 95% said it was too costly, 95% cite the lack of interoperability, 90% cite the lack of funding or return on investment and 88% said integration between various EMRs and other sources of data was a barrier to interoperability.

So what you’ve got here is groups of providers who are expected to deliver efficient, coordinated care or risk financial penalties, but don’t have the ability to track patients moving from provider to provider effectively. This is a recipe for disaster for ACOs, which are having trouble controlling risk even without the added problem of out of synch health IT systems.

By the way, if ACOs hope to make things easier by merging with some of the partners, that may not work either. The FTC — the government’s antitrust watchdog — has begun to take a hard look at many hospital and physician mergers. While hospitals say that they are acquiring their peers to meet care coordination goals, the FTC isn’t buying it, arguing that doctors and hospitals can generally achieve the benefits of coordinated care without a full merger.

This leaves ACOs in a very difficult position. If they risk the FTC’s ire by merging with other providers, but can’t achieve interoperability as separate entities, how are they going to meet the goals they are required to meet by health insurers? (I think there’s little doubt, at this point, that truly successful ACOs will have to find a way to integrate health IT systems smoothly.)  It’s an ugly situation that’s only likely to get uglier.

EMR Change Cuts Cardiac Telemetry Use Substantially

Posted on September 25, 2014 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

Changing styles of medical practice can be really tough, even if major trade organization sticks its oar in to encourage new behavior from docs.

Such is the situation with cardiac telemetry, which is listed by the American Board of Internal Medicine Foundation as either unnecessary or overused in most cases. But a recent piece of research demonstrated that configuring an EMR to help doctors comply with the guideline can help hospitals lower needless cardiac monitoring substantially.

Often, it takes a very long time to get doctors to embrace new guidelines like these, despite pressure from payers, employers and even peers. (Physicians may turn on a dime and try out a new drug when the right pharmaceutical rep shows up, but that’s another story.) Doctors say they stick to their habits because of patient, institutional or personal preferences, as well as fear of lawsuits.

But according to a recent study appearing in JAMA Internal Medicine, reprogramming its Centricity EMR did the trick for Wilmington, Del.-based Christiana Care Health System.

To curb the use of cardiac telemetry that was unnecessary, Christiana Care removed the standard option for doctors to order cardiac monitoring outside of AHA guidelines, and required them to take an extra step to order this type of test.

Meanwhile, when the cardiac monitoring order did fall within AHA guidelines, Christiana Care added an AHA-recommended time frame for the monitoring. After that time passed, the EMR notified nurses to stop the monitoring or ask physicians if they believed it would be unsafe to stop.

The results were striking. After implementing the changes in the EMR, the health systems average daily not intensive care unit patients with cardiac monitoring fell by 70%. What’s more, Christiana Care’s average daily cost of administering  non-ICU cardiac monitoring held by 70%, from $18,971 to $5,772.

Christiana Care’s health IT presence is already well ahead of many hospitals — it’s reached Stage 6 of the HIMSS EMRAM scale — so it’s not surprising to see it leading the way in shaping physician behavior.

The question now is how the system builds on what it’s learned. Having survived a politically-sensitive transition without creating a revolution in its ranks, I’d argue the time is now to jump in and work on compliance with other clinical guidelines. With pressure mounting to deliver efficient care, it’d be smart to keep the ball rolling.

Can Big Data Do What Vendors Claim?

Posted on December 6, 2013 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

There’s no doubt about it — the air is ringing with the sounds of vendors promising big things from big data, from population health to clinical support to management of bundled payments. But can they really offer these blessings?  According to enterprise health IT architect Michael Planchart (known to many as @theEHRGuy), there’s a lot of snake oil sales going on.

In his experience, many of the experts on what he calls Big Bad Data either weren’t in healthcare or have never touched healthcare IT until the big data trend hit the industry. And they’re pitching the big data concept to providers that aren’t ready, he says:

  • Most healthcare providers haven’t been collecting data in a consistent way with a sound data governance model.
  • Most hospitals have paper charts that collect data in unstructured and disorganized ways.
  • Most hospitals — he asserts — have spent millions or even billions of dollars on EMRs but have been unable to implement them properly. (And those that have succeeded have done so in “partial and mediocre ways,” he says.)

Given these obstacles,  where is big data going to come from today? Probably not the right place, he writes:

Well, some geniuses from major software vendors thought they could get this data from the HL7 transactions that had been moving back and forth between systems.  Yes, indeed.  They used some sort of “aggregation” software to extract this data out of HL7 v2.x messages.  What a disaster!  Who in their sane mind would think that transactional near real time data could be used as the source for aggregated data?

As Planchart sees it, institutions need quality, pertinent, relevant and accurate data, not coarsely aggregated data from any of the sources hospitals and providers have. Instead of rushing into big data deals, he suggests that CIOs start collecting discrete, relevant and pertinent data within their EMRs, a move which will pay off over the next several years.

In the mean time, my colleague John Lynn suggests, it’s probably best to focus on “skinny data” — a big challenge in itself given how hard it can be to filter out data “noise” — rather than aggregate a bunch of high volume data from all directions.

EMRs Can Create New Malpractice Problems

Posted on October 9, 2013 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

In theory, EMRs have the capacity to improve patient care and avoid medical errors, but they also stand the chance of creating errors of their own, so users shouldn’t expect EMRs to lower their malpractice premiums, according to a story reported in FierceEMR.

In fact, the story suggests, EMRs can create new problems and make it harder to defend against lawsuits arising from some EMR-related problems, including the following, FierceEMR notes:

  • Disabled clinical decision support alerts that, if used, could have caught a problem
  • Auto complete functions that fill in data incorrectly
  • Sharing of passwords, so that physicians look like they’re viewing the chart when they really aren’t or in more than one place at the same time
  • Sloppy documentation, such as data entered incorrectly

What’s more, EMRs create audit trails which make it easier for plaintiff’s attorneys to find errors in care. And on top of that, legal costs for “e-discovery” — the collection of evidence from electronic systems — can raise the expense of a legal battle further, FierceEMR says.

Here’s an example of a situation in which an EMR-based error can create serious legal exposure. In one case lodged against the  University of Pittsburgh Medical Center, a 62-year old man died due to otherwise treatable bleeding in the brain because an intubation failed.

UPMC’s policy is that when a patient is a difficult intubation case, that must be noted directly in the EMR, which then displays a bright yellow banner nothing the problems at the top of the record. However, “difficult intubation” was not noted in his chart.  When his breathing tubes were later removed, he could not continue to breathe on his own. Attempts to re-intubate him failed, and he died.

As if that wasn’t bad enough, the defense alleges that after the patient’s death, a QA official from UPMC accessed the system and retroactively entered data labeling the deceased as a difficult intubaton. When that didn’t create the yellow banner, the defense claims, the official retracted the “diff intub” entry. Unfortunately for him, all of his actions were logged by the system.

The bottom line is that as it becomes apparent that EMRs come with their own set of safety issues, malpractice insurers who once offered premium discounts to those who use EMRs are dropping the idea. EMRs certainly have the potential to offer improved safety in some instances, but human error isn’t going away completely no matter what fixes EMRs offer.

EMRs Can Reduce ED Visits, Hospitalizations For Diabetics

Posted on September 16, 2013 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of 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. She can be reached at @ziegerhealth or

Using EMRs is associated with a drop in ED visits and hospitalizations among diabetics, according to a study covered in iHealthBeat.

The research, which appeared in the Journal of the American Medical Association, involved analyzing all of the 169,711 records for patients enrolled in Kaiser Permanente Northern California’s diabetes registry.

Researchers drew on data collected between 2004 and 2009. During this period, in 2005, KP began to stagger EMR implementations across the region’s 45 outpatient facilities, iHealthBeat reports.

The study found that EMR implementations were associated with the following results, according to iHealthBeat:

  • 10.50% decline in hospitalizations for preventable, ambulatory-care sensitive conditions, or about 7.08 fewer hospitalizations per 1,000 patients annually;
  • 6.14% decline in non-elective hospital admissions, or about 10.92 fewer admissions per 1,000 patients annually;
  • 5.54% decline in ED visits, from an expected 519.12 per 1,000 patients to 490.32 annually; and
  • 5.21% decline in hospital admissions, from an expected 251.6 per 1,000 patients to 238.5 annually

That being said, EMR implementation had no effect in certain areas. The number of physician office visits per year held steady at six; the frequency of times patients saw diabetic exacerbations remained level; and how often patients developed cardiovascular diseases remained the same, iHealthBeat noted.

The researchers concluded that these results represented not only an improvement in diabetes care, but also “the cumulative effect of EHRs across many different pathways and conditions.

This study is one of a growing body of evidence that effective EMR  use can reduce readmissions and improve outcomes.  For example, a recent study appearing in BMJ Quality & Safety recently concluded that EMRs can help reduce hospital readmissions of high-risk heart failure patients.

In that case, researchers used EMR-based software to sort high-risk from low-risk heart failure patients, using 29 clinical, social and behavioral factors within 24 hours of admission for heart failure. Using this tool, researchers were able to cut readmissions rates for the 1,700 adult inpatients study from 26.2 percent to 21.2 percent.