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Longitudinal Patient Record Needed To Advance Care?

Posted on November 23, 2016 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 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

In most day to day settings, a clinician only needs a small (if precisely focused) amount of data to make clinical decisions. Both in ambulatory and acute settings, they rely on immediate and near-term information, some collected during the visit, and a handful of historical factors likely to influence or even govern what plan of care is appropriate.

That may be changing, though, according to Cheryl McKay of Orion Health. In a recent blog item, McKay argues that as the industry shifts from fee-for-service payment models to value-based reimbursement, we’ll need new types of medical records to support this model. Today, the longitudinal patient record and community care plan are emerging as substitutes to old EMR models, McKay says. These new entities will be built from varied data sources including payer claims, provider EMRs, patient health devices and the patients themselves.

As these new forms of patient medical record emerge, effective population health management is becoming more feasible, she argues. Longitudinal patient records and community care plans are “essential as we steer away from FFS…The way records are delivered to healthcare providers– with an utter lack of visibility and a lot of noise from various data sources– creates unnecessary risks for everyone involved.”

She contends that putting these types of documentation in place, which summarize patient-based clinical experiences versus episodic clinical experiences, close big gaps in patient history which would otherwise generate mistakes. Longitudinal record-keeping also makes it easier for physicians to aggragate information, do predictive modeling and intervene proactively in patient care at both the patient and population level.

She also predicts that with both a longitudinal patient record and community care plan in place, getting from the providers of all stripes a “panoramic” look at patients, costs will fall as providers stop performing needless tests and procedures. Not only that, these new entities would ideally offer real-time information as well, including event notifications, keeping all the providers involved in sync in providing the patient’s care.

To be sure, this blog item is a pitch for Orion’s technology. While the notion of a community-care plan isn’t owned by anyone in particular, Orion is pitching a specific model which rides upon its population health technology. That being said, I’m betting most of us would agree that the idea (regardless of which vendor you work with) of establishing a community-wide care plan does make sense. And certainly, putting a rich longitudinal patient record in place could be valuable too.

However, given the sad state of interoperability today, I doubt it’s possible to build this model today unless you choose a single vendor-centric solution. At present think it’s more of a dream than a reality for most of us.

Managing Health Information to Ensure Patient Safety

Posted on August 17, 2016 I Written By

Erin Head is the Director of Health Information Management (HIM) and Quality for an acute care hospital in Titusville, FL. She is a renowned speaker on a variety of healthcare and social media topics and currently serves as CCHIIM Commissioner for AHIMA. She is heavily involved in many HIM and HIT initiatives such as information governance, health data analytics, and ICD-10 advocacy. She is active on social media on Twitter @ErinHead_HIM and LinkedIn. Subscribe to Erin's latest HIM Scene posts here.

This post is part of the HIM Series of blog posts. If you’d like to receive future HIM posts by Erin in your inbox, you can subscribe to future HIM Scene posts here.

Electronic Medical Records (EMRs) have been a great addition to healthcare organizations and I know many would agree that some tasks have been significantly improved from paper to electronic. Others may still be cautious with EMRs due to the potential patient safety concerns that EMRs bring to light.

The Joint Commission expects healthcare organizations to engage in the latest health information technologies but we must do so safely and appropriately. In 2008, The Joint Commission released Sentinel Event Alert Issue 42 which advised organizations to be mindful of the patient safety risks that can result from “converging technologies”.

The electronic technologies we use to gather patient data could pose potential threats and adverse events. Some of these threats include the use of computerized physician order entry (CPOE), information security, incorrect documentation, and clinical decision support (CDS).  Sentinel Event Alert Issue 54 in 2015 again addressed the safety risks of EMRs and the expectation that healthcare organizations will safely implement health information technology.

Having incorrect data in the EMR poses serious patient safety risks that are preventable which is why The Joint Commission has put this emphasis on safely using the technology. We will not be able to blame patient safety errors on the EMR when questioned by surveyors, especially when they could have been prevented.

Ensuring medical record integrity has always been the objective of HIM departments. HIM professionals’ role in preventing errors and adverse events has been apparent from the start of EMR implementations. HIM professionals should monitor and develop methods to prevent issues in the following areas, to name a few:

Copy and paste

Ensure policies are in place to address copy and paste. Records can contain repeated documentation from day to day which could have been documented in error or is no longer current. Preventing and governing the use of copy and paste will prevent many adverse issues with conflicting or erroneous documentation.

Dictation/Transcription errors

Dictation software tools are becoming more intelligent and many organizations are utilizing front end speech recognition to complete EMR documentation. With traditional transcription, we have seen anomalies remaining in the record due to poor dictation quality and uncorrected errors. With front end speech recognition, providers are expected to review and correct their own dictations which presents similar issues if incorrect documentation is left in the record.

Information Security

The data that is captured in the EMR must be kept secure and available when needed. We must ensure the data remains functional and accessible to the correct users and not accessible by those without the need to know. Cybersecurity breaches are a serious threat to electronic data including those within the EMR and surrounding applications.

Downtime

Organizations must be ready to function if there is a planned or unexpected downtime of systems. Proper planning includes maintaining a master list of forms and order-sets that will be called upon in the case of a downtime to ensure documentation is captured appropriately. Historical information should be maintained in a format that will allow access during a downtime making sure users are able to provide uninterrupted care for patients.

Ongoing EMR maintenance

As we continue to enhance and optimize EMRs, we must take into consideration all of the potential downstream effects of each change and how these changes will affect the integrity of the record. HIM professionals need prior notification of upcoming changes and adequate time to test the new functionality. No changes should be made to an EMR without all of the key stakeholders reviewing and approving the changes downstream implications. The Joint Commission claims, “as health IT adoption becomes more widespread, the potential for health IT-related patient harm may increase.”

If you’d like to receive future HIM posts by Erin in your inbox, you can subscribe to future HIM Scene posts here.

EHRs Can Help Find Patients At High Risk Of Dying

Posted on June 1, 2016 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 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Much of the discussion around EMRs and EHRs these days focuses on achieving broad, long-term goals such as improved population health. But here’s some data suggesting that these systems can serve a far more immediate purpose – finding inpatients at imminent risk of death.

A study appearing in The American Journal of Medicine details how researchers from Arizona-based Banner Health created an algorithm looking for key indicators suggesting that patients were in immediate danger of death. It was set up to send an alert when patients met at least two of four systemic inflammatory response syndrome criteria, plus at least one over 14 acute organ dysfunction parameters. The algorithm was applied in real time to 312,214 patients across 24 hospitals in the Banner system.

Researchers found that the alert was able to identify the majority of high-risk patients within 48 hours of their admission to a hospital, allowing clinical staff to deliver early and targeted medical interventions.

This is not the first study to suggest that clinical data analysis can have a significant impact on patients’ health status. Research from last year on clinical decision support tools appearing in Generating Evidence & Methods to Improve Patient Outcomes found that such tools can be beefed up to help providers prevent stroke in vulnerable patients.

In that study, researchers from Ohio State University created the Stroke Prevention in Healthcare Delivery Environments tool to pull together and display data relevant to cardiovascular health. The idea behind the tool was to help clinicians have more effective discussions with patients and help address risk factors such as smoking and weight.

They found that the tool, which was tested at two outpatient settings at Ohio State University’s Wexner Medical Center, garnered a “high” level of satisfaction from providers. Also, patient outcomes improved in some areas, such as diabetes status and body mass index.

Despite their potential, few tools are in place today to achieve such immediate benefits as identifying inpatients at high risk of death. Certainly, clinicians are deluged with alerts, such as the ever-present med interaction warnings, but alerts analyzing specific patients’ clinical picture aren’t common. However, they should be. While drug warnings might irritate physicians, I can’t see them ignoring an alert warning them that the patient might die.

And I can hardly imagine a better use of EMR data than leveraging it to predict adverse events among sick inpatients. After all, few hospitals would spend dozens or hundreds of millions of dollars to implement the system which creates a repository that simply mimics paper records.

In addition to preventing adverse events, real-time EMR data analytics will also support the movement to value-based care. If the system can predict which patients are likely to develop expensive complications, physicians can do a better job of preventing them. While clinicians, understandably, aren’t thrilled will being told how to deliver care, they are trained to respond to problems and solve them.

I’m hoping to read more about technologies that leverage EMR data to solve day-to-day care problems. This is a huge opportunity.

Can HIM Professionals Become Clinical Documentation Improvement Specialists?

Posted on April 21, 2016 I Written By

Erin Head is the Director of Health Information Management (HIM) and Quality for an acute care hospital in Titusville, FL. She is a renowned speaker on a variety of healthcare and social media topics and currently serves as CCHIIM Commissioner for AHIMA. She is heavily involved in many HIM and HIT initiatives such as information governance, health data analytics, and ICD-10 advocacy. She is active on social media on Twitter @ErinHead_HIM and LinkedIn. Subscribe to Erin's latest HIM Scene posts here.

Most acute care hospitals have implemented a clinical documentation improvement (CDI) program to drive appropriate reimbursement and clarification of documentation. These roles typically live (and should live) within the HIM department. Clinical Documentation Specialists (CDS) work closely with the medical staff and coders to ensure proper documentation and must have an understanding of coding and reimbursement methodologies along with clinical knowledge.

Certain aspects of the CDI or CDS role require in-depth clinical knowledge and experience to read and understand what documentation is already in the chart and find what is missing. Some diagnoses may be hiding in ambiguous documentation and it is up to the CDS to gather consensus from the medical staff to clarify through front-end queries. There are many tools available to assist in this process by creating worklists and documentation suggestions based on diagnosis criteria and best practices. The focus of CDI is not entirely on reimbursement, although it is a nice reward to receive appropriate reimbursement for the treatment provided while obtaining compliant documentation for regulatory purposes.

Determining or changing the potential DRG prior to discharging a patient provides a secondary data source for many healthcare functions such as case management, the plan of care, decision support, and alternative payment models. For these reasons, a CDS must know the coding guidelines for selecting a principal diagnosis that will ultimately determine the DRG.

Inpatient coders also have the foundational skills to perform this role. Coders and HIM professionals are required to have advanced knowledge of anatomy and physiology, pharmacology, and clinical documentation. Therefore, to answer my original question “Can HIM professionals become Clinical Documentation Improvement Specialists?”, the answer is absolutely. But I will say that it depends on the organization as to whether nursing licensure and clinical experience is required in the job description.

Some organizations have mixed CDI teams consisting of coders and nurses while others may allow only nurses to qualify for this role. The impact of who performs the CDS role in the CDI program all lies in the understanding of the documentation, knowledge of coding guidelines, and detective work to remedy missing or conflicting documentation.

If you’d like to receive future HIM posts by Erin in your inbox, you can subscribe to future HIM Scene posts here.

A Look At Precision Medicine Solutions Available Today

Posted on December 22, 2015 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.

Personalized and Precision Medicine are all the buzz since President Obama announced the Precision Medicine Initiative. However, after the government tragedy known as meaningful use, many are reasonably skeptical of government initiatives to improve healthcare. Plus, the rhetoric around what’s possible with precision medicine and the realities that most hospitals and doctors face every day feels like a massive disconnect.

The reality is that there’s good reason to be skeptical of precision medicine. Think about the scope of the problem. The world of health data that we live in today is 10-20 times bigger that it was even a decade ago. That’s a massive increase in the amount of data available. Plus, much of that data is unstructured data. Combine the volume of data with the accessibility (or lack therof) of that data and it’s easy to see why some are skeptical of really implementing precision medicine in their hospital today.

When you look at current EHR systems, none of them are built to enable precision medicine. First, they were built as massive billing engines and not as engines designed to improve care. Second, meaningful use has hijacked their development roadmap for years and will likely continue to hijack their development teams for years to come. Finally, there’s been so much money doing what they’re doing, what motivation do the entrenched EHR companies have to go out and do more?

The unfortunate reality of EHR systems is that they’re not built for real time availability of data analytics that provides improved care and precision, personalized medicine. Some may get there eventually, but we’re unlikely to see them get there anytime soon. I’ve heard precision medicine defined as a puzzle with 3 billion pieces. We have to start looking outside of traditional EHR companies to start solving such a complex puzzle.

The good news is that even though EHR vendors are not providing precision medicine solutions, we’re starting to see other vendors providing precision medicine solutions today. You no longer need to wait for an EHR vendor to participate.

One example of precision medicine happening today is the recently announced SAP Foundation for Health (we’ll forgive them on the somewhat confusing name). At the core of the SAP Foundation for Health is the SAP Hana engine. Unlike many EHR systems, SAP Hana was designed for real time data analysis of massive amounts of data and that includes both granular and free form data. You can see this capability first hand in the work SAP is doing with ASCO (American Society of Clinical Oncology) and their CancerLinQ project.

Dr. Clifford Hudis from CancerLinQ (Created by ASCO) described how personalized medicine to his grandfather was going around and visiting each patient. Over time that practice stopped and we started seeing patients in clinics where we generally only had one data set available to us: the clinical data that we captured ourselves on a paper chart. Unfortunately, as we moved electronic, we just recreated our paper chart world in electronic form. It’s too bad we didn’t do more during our shift to going electronic. However, that still means we have the opportunity to aggregate and analyze health data for the benefit of our patients. In some ways, we’re starting to democratize access to health data in order to enable precision medicine.

As Dr. Hudis pointed out, healthcare currently really only learns from patients who take part in clinical research trials. In other words, that only amounts to about 3% of adult patients who contribute to our learning. This limits our view since most clinical research trials have a biased sample which aren’t representative of the general population. How can we create personalized medicine if we only have data on 3% of the patient population? This is the problem CancerLinQ and SAP Foundation for Health are working to solve. Can they create a platform that learns from every patient?

ASCO together with SAP’s Foundation for Health is working to aggregate and analyze data across cancer patients regardless of whether they’re part of a clinical research study or not. In the past, Dr. Hudis pointed out that cancer tracking use to track cancer populations with simple groups like “small cell cancer” versus “non-small cell cancer.” That was a start, but had limited precision when trying to treat a patient. With this relatively new world of genomics, ASCO can now identify, track, and compare a patient’s cancer by specific genomic alterations. This is a fantastic development since tumors generally contain changed DNA. We can now use these DNA abnormalities to classify and track cancer patients in a much more precise way than we’ve done in the past.

This platform enables oncologists the opportunity to see real time information about their patient that’s personalized to the patients own genetic abnormalities. Instead of calling around to their network of oncologist friends, Cancer LinQ provides real time access to other patient populations with similar genetic abnormalities and could give them insight into what treatments are working for similar patients. This can also provide benchmarking for oncologists to see how they compare against their colleagues. Plus, it can show real time data to an oncologist so they can know how thorough and consistent they are with their patient population. Instead of working in a bubble, the oncologist can leverage the network of data to provide true precision medicine for their patients.

Another great example of precision medicine happening today is seen in the work of Carlos Bustamante, Professor of Genetics and Stanford University School of Medicine. Carlos is using SAP Foundation for Health to quickly identify genetic abnormalities in high performing athletes. Rather than recount the stories of Carlos’ work here, I’ll just link to this video where Carlos talks about the amazing insights they’ve found from studying the genomic abnormalties of high performing athletes. I love that his precision medicine work with high performing athletes has significant potential benefits for every patient.

Carlos is spot on in the video linked above when he says that the drop in genomic sequencing costs would be like taking a $400,000 Ferrari and now selling it for 10 cents. What originally took $13 billion and years of effort to sequence the first genome now takes $1500 and a few days. Access to every patient’s genome is going to change the types of drugs we develop, the treatment options we provide patients, our choice of drugs to treat a patient, and much much more. You can see that first hand in the work that ASCO and Stanford University School of Medicine are doing. Is there any more personalized medicine than the human genome?

Of course, the genome is just one of the many factors we’re seeing in the precision medicine revolution. We can’t forget about other variables that impact a patient’s health like environmental, behavioral, patient preference, and much more. We really are looking at a multi-billion piece puzzle and we’re just getting started. Remember that healthcare is not linear, but we’ve been treating it like it is for years. Healthcare is a complex matrices of challenges and we need our technology solutions to reflect that fact.

I see a beautiful future for precision medicine that’s already begun and builds into the future. We’re developing and targeting new drugs, devices and services that work for populations and individuals. We’re seeing new open, secure platforms that provide real-time flexible R&D analysis, genomics and other “omics” disciplines, patient cohort building and analysis, patient trial matching, and extended care collaboration solutions.

Data by itself is not valuable. However, the right engine on top of the right data is changing how we look at healthcare. We’re getting a much more precise view of each individual patient. Where have you seen precision medicine starting to take hold? What precision medicine solutions are you using in your organization?

Also, check out this infographic which looks at SAP’s view of precision medicine:
Personalized Medicine You Can Do Today

SAP is uniquely positioned to help advance personalized medicine. The SAP Foundation for Health is built on the SAP Hana platform which provides scalable cloud analytics solutions across the spectrum of healthcare. SAP is a sponsor of Influential Networks of which Healthcare Scene is a member.

Not So Far Far Away From Star Wars Medical Droids

Posted on December 18, 2015 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin is a true believer in #HealthIT, social media and empowered patients. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He currently leads the marketing efforts for @PatientPrompt, a Stericycle product. Colin’s Twitter handle is: @Colin_Hung

Friday December 18th is the day that Star Wars: The Force Awakens hit theatres. It carries with it the dreams of generations of fans. From old timers like me (who remember watching Star Wars: A New Hope in a converted opera house in 1977) to the new generation who grew up watching the prequels and the Clone Wars – everyone is looking forward to this new film.

As a fan, I thought it would be remiss of me if I didn’t write a blog using Star Wars as the theme this week.

One of the things that always struck me about Star Wars was the lack of doctors in the movies. Unlike the Star Trek universe where we had the lovable character of Dr. Leonard McCoy (Bones), you never really see a physician in Star Wars. Instead all the healing is done by droids.

In Empire Strikes Back, we are introduced to a medical droid that heals Luke Skywalker after his encounter with the abominable snowman-like Wampa on the frozen planet of Hoth. At the end of the movie we see other droids caring for Luke after he loses his hand after battling Darth Vader.

Back in the 80s when Empire Strikes Back was released these medical droids were pure science fiction. In 2015 medical robots are a reality and some are surprisingly similar to the ones depicted in the movie. Take for example the da Vinci Surgical Robot by Intuitive Surgical (on the left) which looks like a precursor version to the FX series of medical droids from Star Wars (on the right).

Da Vinci Xi Robot and Star Wars FX Medical Droid

I’ve never seen the da Vinci surgical robot, but the write-ups have been incredible. This robot allows surgeons to perform minimally invasive surgeries using the four finely controlled arms. The surgeon controls everything through a console. It is not hard to imagine that one day soon the surgeon performing the surgery may not be in the same hospital or even the same country as the robot itself – the ultimate in telemedicine!

Surgical robots are a hot area of healthcare innovation. Just last week Johnson & Johnson and Verily Life Sciences (formerly Google Life Sciences) got together to create Verb Surgical. According to the press release, “in the coming years, Verb Surgical aims to develop a comprehensive surgical solutions platform that will incorporate leading-edge robotic capabilities and best-in-class medical device technology for operating room professionals”.

As more companies enter this space, the faster these robots will evolve.

However, having articulating surgical robots only gets us part-way to a fully functional Start Wars medical droid. We have the body, but now we need the brains. That’s where IBM’s Watson comes in.

Watson is arguably the closest thing we currently have to artificial intelligence. IBM’s brainchild is able to analyze data and draw patterns/conclusions faster than any computer system that has ever existed. It is already capable of crunching through millions medical records and use that knowledge to help with cancer treatment. In pilots with several institutions, Watson is already assisting with diagnosis and treatment of disease.

It’s not hard to imagine that one day a Watson-like system will be combined with a surgical robot. Add in a little bit of advanced machine vision plus a few antimicrobial nanomaterials and all of a sudden you have the basics of a Star Wars medical droid.

The optimist in me believes it will happen in my lifetime. I only wish lightsabers and x-wing fighters weren’t so far far away.

Image Credit

Da Vinci Xi Robot – engadget http://www.engadget.com/2014/04/01/da-vinci-xi-surgical-robot/

FX medial droid – starwars.wikia.com http://starwars.wikia.com/wiki/FX-series_medical_assistant_droid

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 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

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 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

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.