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Connecting the Data: Three Steps to Meet Digital Transformation Goals

Posted on July 16, 2018 I Written By

The following is a guest blog post by Gary Palgon, VP Healthcare and Life Sciences Solutions at Liaison Technologies.

A white paper published by the World Economic Forum in 2016 begins with the statement, “Few industries have the potential to be changed so profoundly by digital technology as healthcare, but the challenges facing innovators – from regulatory barriers to difficulties in digitalizing patient data – should not be underestimated.”

That was two years ago, and many of the same challenges still exist as the digital transformation of healthcare continues.

In a recent HIMSS focus group sponsored by Liaison, participants identified their major digital transformation and interoperability goals for the near future as:

  • EMR rollout and integration
  • Population health monitoring and analytics
  • Remote clinical encounters
  • Mobile clinical applications

These goals are not surprising. Although EMRs have been in place in many healthcare organizations for years, the growth of health systems as they add physicians, clinics, hospitals and diagnostic centers represents a growing need to integrate disparate systems. The continual increase in the number of mobile applications and medical devices that can be used to gather information to feed into EMR systems further exacerbates the challenge.

What is surprising is the low percentage of health systems that believe that they are very or somewhat well-prepared to handle these challenges – only 35 percent of the HIMSS/Liaison focus group members identified themselves as well-prepared.

“Chaos” was a word used by focus group participants to describe what happens in a health system when numerous players, overlapping projects, lack of a single coordinator and a tendency to find niche solutions that focus on one need rather than overall organizational needs drive digital transformation projects.

It’s easy to understand the frustration. Too few IT resources and too many needs in the pipeline lead to multiple groups of people working on projects that overlap in goals – sometimes duplicating each other’s efforts – and tax limited staff, budget and infrastructure resources. It was also interesting to see that focus group participants noted that new technologies and changing regulatory requirements keep derailing efforts over multi-year projects.

Throughout all the challenges identified by healthcare organizations, the issue of data integrity is paramount. The addition of new technologies, including mobile and AI-driven analytics, and new sources of information, increases the need to ensure that data is in a format that is accessible to all users and all applications. Otherwise, the full benefits of digital transformation will not be realized.

The lack of universal standards to enable interoperability are being addressed, but until those standards are available, healthcare organizations must evaluate other ways to integrate and harmonize data to make it available to the myriad of users and applications that can benefit from insights provided by the information. Unlocking access to previously unseen data takes resources that many health organizations have in short supply. And the truth is, we’ll never have the perfect standards as they will always continue to change, so there’s no reason to wait.

Infrastructure, however, was not the number one resource identified in the HIMSS focus group as lacking in participants’ interoperability journey. In fact, only 15 percent saw infrastructure as the missing piece, while 30 percent identified IT staffing resources and 45 percent identified the right level of expertise as the most critical needs for their organization.

As all industries focus on digital transformation, competition for expert staff to handle interoperability challenges makes it difficult for healthcare organizations to attract the talent needed. For this reason, 45 percent of healthcare organizations outsource IT data integration and management to address staffing challenges.

Health systems are also evaluating the use of managed services strategies. A managed services solution takes over the day-to-day integration and data management with the right expertise and the manpower to take on complex work and fluctuating project levels. That way in-house staff resources can focus on the innovation and efficiencies that support patient care and operations, while the operating budget covers data management fees – leaving capital dollars available for critical patient care needs.

Removing day-to-day integration responsibilities from in-house staff also provides time to look strategically at the organization’s overall interoperability needs – coordinating efforts in a holistic manner. The ability to implement solutions for current needs with an eye toward future needs future-proofs an organization’s digital investment and helps avoid the “app-trap” – a reliance on narrowly focused applications with bounded data that cannot be accessed by disparate users.

There is no one answer to healthcare’s digital transformation questions, but taking the following three steps can move an organization closer to the goal of meaningful interoperability:

  • Don’t wait for interoperability standards to be developed – find a data integration and management platform that will integrate and harmonize data from disparate sources to make the information available to all users the way they need it and when they needed.
  • Turn to a data management and integration partner who can provide the expertise required to remain up-to-date on all interoperability, security and regulatory compliance requirements and other mandatory capabilities.
  • Approach digital transformation holistically with a coordinated strategy that considers each new application or capability as data gathered for the benefit of the entire organization rather than siloed for use by a narrowly-focused group of users.

The digital transformation of healthcare and the interoperability challenges that must be overcome are not minor issues, nor are they insurmountable. It is only through the sharing of ideas, information about new technologies and best practices that healthcare organizations can maximize the insights provided by data shared across the enterprise.

About Gary Palgon
Gary Palgon is vice president of healthcare and life sciences solutions at Liaison Technologies, a proud sponsor of Healthcare Scene. In this role, Gary leverages more than two decades of product management, sales, and marketing experience to develop and expand Liaison’s data-inspired solutions for the healthcare and life sciences verticals. Gary’s unique blend of expertise bridges the gap between the technical and business aspects of healthcare, data security, and electronic commerce. As a respected thought leader in the healthcare IT industry, Gary has had numerous articles published, is a frequent speaker at conferences, and often serves as a knowledgeable resource for analysts and journalists. Gary holds a Bachelor of Science degree in Computer and Information Sciences from the University of Florida.

The Truth about AI in Healthcare

Posted on June 18, 2018 I Written By

The following is a guest blog post by Gary Palgon, VP Healthcare and Life Sciences Solutions at Liaison Technologies.

Those who watched the television show, “The Good Doctor,” in its first season got to see how a young autistic surgeon who has savant syndrome faced challenges in his everyday life as he learns to connect with people in his world. His extraordinary medical skill and intuition not only saves patients’ lives but also creates bridges with co-workers.

During each show, there is at least one scene in which the young doctor “visualizes” the inner workings of the patient’s body – evaluating and analyzing the cause of the medical condition.

Although all physicians can describe what happens to cause illness, the speed, detail and clarity of the young surgeon’s ability to gather information, predict reactions to treatments and identify the protocol that will produce the best outcome greatly surpasses his colleagues’ abilities.

Yes, this is a television show, but artificial intelligence promises the same capabilities that will disrupt all of our preconceived notions about healthcare on both the clinical and the operational sides of the industry.

Doctors rely on their medical training as well as their personal experience with hundreds of patients, but AI can allow clinicians to tap into the experience of hundreds of doctors’ experiences with thousands of patients. Even if physicians had personal experience with thousands of patients, the human mind can’t process all of the data effectively.

How can AI improve patient outcomes as well as the bottom line?

We’re already seeing the initial benefits of AI in many areas of the hospital. A report by Accenture identifies the top three uses of AI in healthcare as robot-assisted surgery, virtual nursing assistants and administrative workflow assistance. These three AI applications alone represent a potential estimated annual benefit of $78 billion for the healthcare industry by 2026.

The benefits of AI include improved precision in surgery, decreased length of stay, reduction in unnecessary hospital visits through remote assessment of patient conditions, and time-saving capabilities such as voice-to-text transcription. According to Accenture, these improvements represent a work time savings of 17 percent for physicians and 51 percent for registered nurses – at a critical time when there is no end in sight for the shortages of both nurses and doctors.

In a recent webinar discussing the role of AI in healthcare, John Lynn, founder of HealthcareScene.com, described other ways that AI can improve diagnosis, treatment and patient safety. These areas include dosage error detection, treatment plan design, determination of medication adherence, medical imaging, tailored prescription medicine and automated documentation.

One of the challenges to fully leveraging the insights and capabilities of AI is the volume of information accumulated in electronic medical records that is unstructured data. Translating this information into a format that can be used by clinical providers as well as financial and administrative staff to optimize treatment plans as well as workflows is possible with natural language processing – a branch of AI that enables technology to interpret speech and text and determine which information is critical.

The most often cited fear about a reliance on AI in healthcare is the opportunity to make mistakes. Of course, humans make mistakes as well. We must remember that AI’s ability to tap into a much wider pool of information to make decisions or recommend options will result in a more deeply-informed decision – if the data is good.

The proliferation of legacy systems, continually added applications and multiple EMRs in a health system increases the risk of data that cannot be accessed or cannot be shared in real-time to aid clinicians or an AI-supported program. Ensuring that data is aggregated into a central location, harmonized, transformed into a usable format and cleaned to provide high quality data is necessary to support reliable AI performance.

While AI might be able to handle the data aggregation and harmonization tasks in the future, we are not there yet. This is not, however, a reason to delay the use of AI in hospitals and other organizations across the healthcare spectrum.

Healthcare organizations can partner with companies that specialize in the aggregation of data from disparate sources to make the information available to all users. Increasing access to data throughout the organization is beneficial to health systems – even before they implement AI tools.

Although making data available to all of the organization’s providers, staff and vendors as needed may seem onerous, it is possible to do so without adding to the hospital’s IT staff burden or the capital improvement budget. The complexities of translating structured and unstructured data, multiple formats and a myriad of data sources can be balanced with data security concerns with the use of a team that focuses on these issues each day.

While most AI capabilities in use today are algorithms that reflect current best practices or research that are programmed by healthcare providers or researchers, this will change. In the future, AI will expand beyond algorithms, and the technology will be able to learn and make new connections among a wider set of data points than today’s more narrowly focused algorithms.

Whether or not your organization is implementing AI, considering AI or just watching its development, I encourage everyone to start by evaluating the data that will be used to “run” AI tools. Taking steps now to ensure clean, easy-to-access data will not only benefit clinical and operational tasks now but will also position the organization to more quickly adopt AI.

About Gary Palgon
Gary Palgon is vice president of healthcare and life sciences solutions at Liaison Technologies, a proud sponsor of Healthcare Scene. In this role, Gary leverages more than two decades of product management, sales, and marketing experience to develop and expand Liaison’s data-inspired solutions for the healthcare and life sciences verticals. Gary’s unique blend of expertise bridges the gap between the technical and business aspects of healthcare, data security, and electronic commerce. As a respected thought leader in the healthcare IT industry, Gary has had numerous articles published, is a frequent speaker at conferences, and often serves as a knowledgeable resource for analysts and journalists. Gary holds a Bachelor of Science degree in Computer and Information Sciences from the University of Florida.

Making Healthcare Data Useful

Posted on May 14, 2018 I Written By

The following is a guest blog by Monica Stout from MedicaSoft

At HIMSS18, we spoke about making health data useful to patients with the Delaware Health Information Network (DHIN). Useful data for patients is one piece of the complete healthcare puzzle. Providers also need useful data to provide more precise care to patients and to reach patient populations who would benefit directly from the insights they gain. Payers want access to clinical data, beyond just claims data, to aggregate data historically. This helps payers define which patients should be included in care coordination programs or who should receive additional disease management assistance or outreach.

When you’re a provider, hospital, health system, health information exchange, or insurance provider and have the data available, where do you start? It’s important to start at the source of the data to organize it in a way that makes insights and actions possible. Having the data is only half of the solution for patients, clinicians or payers. It’s what you do with the data that matters and how you organize it to be usable. Just because you may have years of data available doesn’t mean you can do anything with it.

Historically, healthcare has seen many barriers to marrying clinical and claims data. Things like system incompatibility, poor data quality, or siloed data can all impact organizations’ ability to access, organize, and analyze data stores. One way to increase the usability of your data is to start with the right technology platform. But what does that actually mean?

The right platform starts with a data model that is flexible enough to support a wide variety of use models. It makes data available via open, standards-based APIs. It organizes raw data into longitudinal records. It includes services, such as patient matching and terminology mapping, that make it easy to use the data in real-world applications. The right platform transforms raw data into information that that aids providers and payers improve outcomes and manage risk and gives patients a more complete view of their overall health and wellness.

Do you struggle with making your data insightful and actionable? What are you doing to transform your data? Share your insights, experiences, challenges, and thoughts in the comments or with us on Twitter @MedicaSoftLLC.

About Monica Stout
Monica is a HIT teleworker in Grand Rapids, Michigan by way of Washington, D.C., who has consulted at several government agencies, including the National Aeronautics Space Administration (NASA) and the U.S. Department of Veterans Affairs (VA). She’s currently the Marketing Director at MedicaSoft. Monica can be found on Twitter @MI_turnaround or @MedicaSoftLLC.

About MedicaSoft
MedicaSoft  designs, develops, delivers, and maintains EHR, PHR, and UHR software solutions and HISP services for healthcare providers and patients around the world. MedicaSoft is a proud sponsor of Healthcare Scene. For more information, visit www.medicasoft.us or connect with us on Twitter @MedicaSoftLLC, Facebook, or LinkedIn.

A New Hospital Risk-Adjustment Model

Posted on August 23, 2017 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Virtually all of the risk adjustment models with which I’m familiar are based on retrospective data. This data clearly has some predictive benefits – maybe it’s too cliché to say the past is prologue – and is already in our hands.

To look at just one example of what existing data archives can do, we need go no further than the pages of this blog. Late last year, I shared the story of a group of French hospitals which are working to predict admission rates as much as 15 days in advance by mining a store of historical data. Not surprisingly, the group’s key data includes 10 years’ worth of admission records.

The thing is, using historical data may not be as helpful when you’re trying to develop risk-adjustment models. After all, among other problems, the metrics by which evaluate care shift over time, and our understanding of disease states changes as well, so using such models to improve care and outcomes has its limitations.

I’ve been thinking about these issues since John shared some information on a risk-adjustment tool which leverages relevant patient care data collected almost in real time.

The Midas Hospital Risk Adjustment Model, which is created specifically for single organizations, samples anywhere from 20 to 600 metrics, which can include data on mortality, hospital-acquired complications, unplanned readmission, lengths of stay and charges. It’s built using the Midas Health Analytics Platform, which comes from a group within healthcare services company Conduent. The platform captures data across hospital functional areas and aggregates it for use in care management

The Midas team chooses what metrics to include using its in-house tools, which include a data warehouse populated with records on more than 100 million claims as well as data from more than 800 hospitals.

What makes the Midas model special, Conduent says, is that it incorporates a near-time feed of health data from hospital information systems. One of the key advantages to doing so is that rather than basing its analysis on ICD-9 data, which was in use until relatively recently, it can leverage clinically-detailed ICD-10 data, the company says.

The result of this process is a model which is far more capable of isolating small but meaningful differences between individual patients, Conduent says. Then, using this model, hospitals risk-adjust clinical and financial outcomes data by provider for hospitalized patients, and hopefully, have a better basis for making future decisions.

This approach sounds desirable (though I don’t know if it’s actually new). We probably need to move in the direction of using fresh data when analyzing care trends. I suspect few hospitals or health system would have the resources to take this on today, but it’s something to consider.

Still, I’d want to know two things before digging into Midas further. First, while the idea sounds good, is there evidence to suggest that collecting recent data offers superior clinical results? And in that vein, how much of an improvement does it offer relative to analysis of historical data? Until we know these things, it’s hard to tell what we’ve got here.

5 Challenges for Healthcare That Won’t Go Away

Posted on May 4, 2016 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.

There are some challenges in healthcare that will likely be with us forever. As soon as we think we have our arms around it, it grows or changes. It’s the nature of life and we have to constantly deal with these challenges as healthcare leaders. In a whitepaper titled “Healthcare Ops Management: 5 Trends You Can’t Ignore In 2016” 5 of these challenges are highlighted:
5 Healthcare Challenges
You can download the full whitepaper for free if you want to dive into more detail on each of these 5 challenges. However, it struck me that these 5 challenges are healthcare challenges that likely won’t go away:

  • Patients are Consumers
  • Patient Safety
  • Emergency Preparedness
  • Data-Rich Environment
  • Emphasis on Cost Reduction

Think about the list above. Will patients become less consumers? Will patient safety ever become less of a concern? Disasters are only picking up, so will we ever not need to prepared for emergencies? Can anyone imagine healthcare having less data? Would a leader ever say to not worry about cost reduction?

All of these challenges (and likely others) are things that healthcare leaders are going to have to deal with going forward. I wonder how many healthcare CIOs have a plan for how they’re going to prepare their organization for each of these challenges on an ongoing basis. I’m sure many have some point projects, but likely lack an overall vision for each of these areas. A plan for each of these 5 challenges would be a great place to start.

Healthcare Analytics Biggest Competitor – Excel

Posted on March 16, 2016 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.

This tweet highlighted an interesting observation I had after experiencing so many healthcare analytics pitches going into and at HIMSS. I’ll set aside the email comment for now (email is still very powerful if done right) and instead focus on Excel. Here’s what I discovered about healthcare analytics:

Excel is a healthcare analytics company’s biggest competitor.

It’s crazy to think about, but it’s true. When a healthcare organization is evaluating healthcare analytics platform the “legacy system” that they’re usually trying to replace is Excel. I can’t tell you how many times I heard analytics vendors say that “Hospital A was doing all of this previously on a bunch of Excel spreadsheets.” If you work at a hospital, you know that you have your own garden of Excel spreadsheets that are used to run your healthcare organization as well.

When you think about the features of Excel, it’s no wonder why it’s so popular in healthcare and why it’s a challenging competitor for most healthcare organizations. First, it’s free. Ok, it’s not technically free, but every healthcare organization has to buy it for a lot of reasons so that cost is already in their standard budget. Second, every computer in the organization has a copy of Excel on it. Third, the majority of people in healthcare are familiar with how to use Excel. Since we love to talk about healthcare IT usability, Excel is extremely usable. Fourth, Excel is surprisingly powerful. I know many healthcare analytics organizations could argue its limitations, but Excel is more powerful than most people realize.

That’s not to say that Excel doesn’t have its weaknesses. I’m sure that most organizations have experienced time wasted trying to figure out which Excel file has the accurate data or is the most up to date. No doubt you’ve experienced the multiple copy problem where 2 people are editing the same file and now you have 2 versions of the same file that need to be merged. Document management software has helped with this situation in many regards as it locks the file when someone starts to edit it and things like that. However, it’s still often a problem.

Another problem with Excel as compared with a true analytics platform is when you want to go in and slice and dice the data. What’s possible with a true analytics platform is so much more powerful when you want to really dive in and chop up the data in unique ways.

While possible in Excel, most uses of Excel are backwards facing data analysis and tracking. You can do some near real-time data analysis in Excel, but newer analytics platforms do a much better job of real time analytics using the latest data.

Of course, the biggest problem long term with Excel is that it can’t scale. Once you reach a certain amount of data points or a certain amount of complexity in the data, Excel falls on its face. However, most healthcare organizations are still working on small data, so Excel’s worked fine.

I’m sure there are many more issues. Hopefully some analytics vendors will chime in with more examples in the comments or on their own blogs. However, it’s worth acknowledging that for many organizations it’s really hard for them to find a healthcare analytics solutions that’s so much better than Excel. Plus, many of these expensive analytics solutions fail when it comes to some of the things that makes Excel great (ie. Free, Usable, Ubiquitous).

Future Ready Technology and Data Discussion at the Dell Healthcare Think Tank

Posted on March 11, 2016 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’m lucky enough to be heading to the SXSW conference again this year. I’m excited to see what interesting things are being said and done at a conference like SXSW. The broad variety of people that attend SXSW provide unique insights and perspectives that you often can’t find at other conferences. I’m sure I’ll be doing a post or two about the things I find at SXSW. Let me know if there’s something I should see while I’m there.

During SXSW I’m also going to slip away from the SXSW activities in order to take part in the Dell Healthcare Think Tank event (not associated with SXSW). I believe this is the 4th year I’ve been able to participate in the event and Dell always does a great job bringing together amazing people to talk about the challenges of healthcare IT. This year I expect no different.

The great part of the Dell Healthcare Think Tank event is that the full event is live streamed for free online so you can watch the discussion no matter where you’re at on Tuesday March 15th from 1-4 PM CT. Plus, the #DoMoreHIT hashtag on Twitter will be extremely alive during the Think Tank event. So you can follow along and even add your own comments and questions on the hashtag as you participate in the event from wherever you might be. Don’t be surprised if we bring up a Twitter comment on the live stream.

This year Dell has done a great job bringing together a diverse panel from many parts of healthcare and I’m especially excited by a number of panelists that represent the patient voice in the discussion. You can see the full list of moderators and panelists below.

Moderators:

  • Mandi Bishop – Healthcare Analytics Innovations & Consulting Practice Lead, Dell, #HIT100 influencer,@MandiBPro
  • Nick van Terheyden, MD – Chief Medical Officer, Dell, @drnic1

Panelists:

Future Ready Technology and Data in Healthcare - #DoMoreHIT

I hope you’ll take the time and join me on the 2016 #DoMoreHIT Healthcare Think Tank live stream and #DoMoreHIT hashgtag on Tuesday March 15th from 1-4 PM CT.

Will Data Dominate Healthcare Headlines in 2016?

Posted on January 15, 2016 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 speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He is currently an independent marketing consultant working with leading healthIT companies. Colin is a member of #TheWalkingGallery. His Twitter handle is: @Colin_Hung.

The lethargy of the first few days after the holidays are the perfect time for contemplation of the year ahead. 2016, in my opinion, is shaping up to be an inflection point for healthcare:

  • Meaningful Use is entering its final stage (good riddance!)
  • The full impact of ICD-10 will begin to be felt this year
  • High deductible plans will cause strain on everyone’s bottom line
  • The US election promises to bring new political headwinds no matter who wins the White House

However there is one topic that I believe will dominate the headline this year – DATA and here’s why.

Data Breaches
IBM declared 2015 to be “The Year of Healthcare Security Breach”. According to their study, over 100 million healthcare records were compromised last year. Unfortunately with healthcare cybersecurity spending lagging behind other industries, health records will remain a relatively easy target for hackers in 2016. Until we bake data security into the design of our systems and processes, healthcare will continue to suffer from high-profile breaches and we will continue to read about them throughout the year.

Personal Health Data
Fitness trackers are everywhere. Market leader FitBit sold 4.8 million devices in the third quarter of 2015, almost double the number from the year before. At the recent Consumer Electronic Show in Las Vegas (#CES16) John Lynn reported that there could be as many as 700 health tracking devices currently on the market. The proliferation of these devices means that we are collecting exponentially more personal health data. As yet, this data has not been used by healthcare providers to assist with diagnosis or treatment of patients. In 2016 I suspect we’ll be hearing a lot about this data – who owns it, how secure it is (or isn’t), how it gets used and when it will be standardized.

Data Sharing (aka interoperability)
The key to unlocking the value of health data is allowing everyone within the healthcare ecosystem to share it in a frictionless manner. That means all doctors, nurses, clinics, hospitals, employers, payers, etc. should be able to easily send and receive patient health data. In 2016 we will be hearing about pioneering organizations who are making data interoperability a priority. We will also hear stories about patients and their employers rising up to tear down the walls of healthcare data silos. Finally, I believe that we will be hearing from a number of startups with unique solutions to the interoperability challenge.

Big Data
Collecting and sharing data is one thing. Deriving meaningful value from that data is a whole different challenge. Luckily that’s where #BigData efforts like IBM’s Watson come in. By tapping into the massive health data stores, Watson’s algorithms are assisting in diagnosis and helping physicians make treatment recommendations. It’s capable of making correlations that would be impossible for a person to do. As more data is made available to Watson, it gets “smarter”. In 2016 we will continue to see Watson and other healthcare #BigData efforts capture headlines as they find new connections between symptoms, disease and treatments.

2016 will be a very interesting year in healthcare. I am excited about the next 350 days. What are you excited about this year? What do you think the big headlines of 2016 will be?

New Data Driven Perspectives in Healthcare w/ @MandiBPro @Ashish_P @techguy

Posted on December 10, 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.

UPDATE: Here’s the recorded version of our interview (Ashish had video issues, so he joined audio only)

As part of our ongoing series of Healthcare Scene interviews (see all our past Healthcare Scene interviews on YouTube), we’re excited to announce our next interview with Mandi Bishop and Ashish Patel where we’ll be talking about New Data Driven Perspectives in Healthcare. If you’d like to watch the interview live and get your questions answered, you can join us on blab, Monday, December 13th at Noon ET (9 AM PT).

In this interviews I’m lucky to have two of the most knowledgeable people in healthcare when it comes to various healthcare data sources and how to extract value out of that data. Plus, they’ll offer ways in which data has changed their perspective on healthcare. I’m also excited to hear about the new data sources that are available for health care and how we are using and will use that data to improve healthcare as we know it.


Here are a few more details about our panelists:

You can watch our interview on Blab or in the embed below. We’ll be interviewing our panelists for the first 30-40 minutes of the blab and then we’ll open up to the audience for questions for the rest of the hour. We hope you can join us live. We’ll also share the recorded video after the event.

Why and Who Should Ensure Quality Health Data?

Posted on August 12, 2015 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.

Contrary to common belief, technology does not own health data. Data exists as a result of the input of multiple sources of information throughout each patient’s healthcare continuum. The data does not exist only because of the technology but rather because of the careful selection of meaningful data items that need to be captured and at what frequency (ie. instantly, daily, weekly, etc.).

We in healthcare collect granular data on anything ranging from demographics, past medical, surgical, and social history, medication dosage and usage, health issues and problem lists, disease and comorbidity prevalence, vital statistics, and everything in between. We collect data on financial performance with benchmarks and reimbursement trends using individual data elements from accounting transactions. Healthcare organizations have been collecting the same or similar data for decades but never before have we been able to operate with such efficiency as we do now thanks to advances in technology.

We have become so data rich in the healthcare environment in a short amount of time and this data continues to multiply daily. But are we still information poor? When we continue to generate data but fail to aggregate the data into quality information, we are essentially wasting bandwidth and storage space with meaningless and disconnected data.

Every time patients have interactions with healthcare providers and facilities, data is generated. Over time, the data that is generated could (and should) be used to paint a picture of trends in patient demographics, population health, best practices in care, comorbidities and disease management, payment models, and clinical outcomes. This information becomes useful in meeting regulatory requirements, overcoming reimbursement hurdles, clinical quality initiatives, and even promotional and marketing material for healthcare organizations. This data could have opposite effects if not properly governed and utilized.

It goes back to the saying “garbage in, garbage out.” If the data cannot be standardized or trusted, it is useless. Input of data must be controlled with data models, hard-stops, templates, and collaborative development of clinical content. Capturing wrong or inconsistent data in healthcare can be dangerous to the patients and healthcare quality measurements as well as leading to unwanted legal actions for clinicians.

So who is the right person for the job of ensuring quality data and information? I have seen bidding wars take place over the ownership of the data and tasks surrounding data analysis, database administration, and data governance. Information Technology/Systems wants to provide data ownership due to the skills in the development and implementation of the technology needed to generate and access data. Clinical Informatics professionals feel they are appropriate for the task due to the understanding of clinical workflow and EHR system optimization. Financial, Accounting, Revenue Integrity, and Decision Support departments feel comfortable handling data but may have motives focused too heavily on the financial impact. Other areas may provide input on clinical quality initiatives and govern clinician education and compliance but may be primarily focused on the input of data instead of the entire data lifecycle.

When searching for an appropriate home for health data and information governance, organizations should look no further than Health Information Management (HIM) professionals. Information management is what HIM does and has always done. We have adapted and developed the data analytics skills needed to support the drive for quality data abstraction and data usage (just look at the education and credentialing criteria). HIM departments are a hub of information, both financial and clinical therefore governing data and information is an appropriate responsibility for this area. HIM also ensures an emphasis on HIPAA guidelines to keep data secure and in the right hands. Ensuring quality data is one of the most important tasks in healthcare today and trusting this task to HIM In collaboration with IT, Informatics, and other departments is the logical and appropriate choice.

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