Healthcare Business Intelligence

What’s Ahead After TEDMED 2013

Last week, a number of TEDMED attendees and myself participated in a Google+ Hangout sponsored by Xerox to take a look back at our unique experiences at TEDMED 2013. The discussion included the following people:

Markus Fromherz, chief innovation officer of Xerox Healthcare
Benjamin Miller, assistant professor at the University of Colorado Denver School of Medicine
Nick Dawson, chief experience officer at Frontier Health Consulting
John Lynn, editor and founder of the Healthcare Scene blog network

We made it a really focused 15 minute discussion of the key takeaways from TEDMED. Some of the topics we discussed included: healthcare big data, multidisciplinary collaboration, citizen science, patient centered care, and a look at TEDMED topics 5-10 years from now. It was a really great discussion, and I encourage you to watch the TEDMED recap video embedded below.

Read more coverage from TEDMED from Xerox on the Real Business at Xerox Blog and follow @XeroxHealthcare.

Post a Comment(0)

May 15, 2013 I Written By John Lynn

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit.

Tags:Citizen Science Healthcare Big Data Multidisciplinary Collaboration Patient Centered Care TedMed TEDMED 2013

How Do You Improve the Quality of EHR Data for Healthcare Analytics?

Written by: John Lynn

A month or so ago I wrote a post comparing healthcare big data with skinny data. I was introduced to the concept of skinny data by Encore Health Resources at HIMSS. I absolutely love the idea of skinny data that provides meaningful results. I wish we could see more of it in healthcare.

However, I was also intrigued by something else that James Kouba, HIT Strategist at Encore Health Resources, told me during our discussion at HIMSS. James has a long background in doing big data in healthcare. He told me about a number of projects he’d worked on including full enterprise data warehouses for hospitals. Then, he described the challenge he’d faced on his previous healthcare data warehouse projects: quality data.

Anyone that’s participated in a healthcare data project won’t find the concept of quality data that intriguing. However, James then proceeded to tell me that he loved doing healthcare data projects with Encore Health Resources (largely a consulting company) because they could help improve the quality of the data.

When you think about the consulting services that Encore Health Resources and other consulting companies provide, they are well positioned to improve data quality. First, they know the data because they usually helped implement the EHR or other system that’s collecting the data. Second, they know how to change the systems that are collecting the data so that they’re collecting the right data. Third, these consultants are often much better at working with the end users to ensure they’re entering the data accurately. Most of the consultants have been end users before and so they know and often have a relationship with the end users. An EHR consultant’s discussion with an end user about data is very different than a big data analyst trying to convince the end user why data matters.

I found this to be a really unique opportunity for companies like Encore Health Resources. They can bridge the gap between medical workflows and data. Plus, if you’re focused on skinny data versus big data, then you know that all of the data you’re collecting is for a meaningful purpose.

I’d love to hear other methods you use to improve the quality of the EHR data. What have you seen work? Is the garbage in leads to garbage out the key to quality data? Many of the future healthcare IT innovations are going to come from the use of healthcare data. What can we do to make sure the healthcare data is worth using?

EMR and Health IT Development – Interview with Chetu

Written by: John Lynn

Craig Schmidt - Chetu
Craig Schmidt is the Director of Global Sales for Healthcare & Pharmaceuticals at Chetu. Craig’s focus at Chetu is understanding the top healthcare industry challenges, creating relationships with HIT leaders and developing Information Technology solutions to address those challenges. Craig has, for over 15 years, held a variety of Sales and Sales Management positions with increasing responsibility in the Healthcare and Information Technology Industries.

Tell us more about Chetu and your work in the healthcare market.

It would not be an exaggeration to say that Chetu has experience in nearly every section of Healthcare IT. In our 13 years we have developed solutions for Providers, Payers, HIT Vendors and others. Just a few of the things with which we have helped customers include: complete EMR and Practice Management design and development, ePrescribing, Drug Database integration, Revenue Cycle Management (835/837 & 270/275 engines).

When does someone in healthcare look to Chetu versus doing the work in house?

The two main reasons are: they do not have the particular HIT experience in-house & they do not have enough “bandwidth” to develop in-house and do not want to hire and train permanent staff.

What’s the most challenging thing about developing applications in healthcare?

Healthcare in general and Healthcare IT are bound by many Federal, State and other rules and regulations, e.g., Meaningful Use, Affordable Care Act, HIPAA, etc. There are also a variety of standards for interoperability such as HL7, CCD/CCR.

Do you mostly do one off projects or long term contracts with your clients?

We strive to be the “Back End, Long-term” IT Partner for our clients. We offer complete solutions from application development and support to maintenance and management of applications and systems. In Healthcare we have many (over 60%) clients that have been working with Chetu for multiple years. Many of these have been with Chetu for over 5 years – which is very long-term in this market

What’s your view on SaaS vs. in house client server applications? Do you have a preferred technology stack? What do you see being used most in healthcare?

For the past several years organizations have been rapidly moving to the “Cloud.” And, there are obvious advantages for being cloud based. However, client server applications have advantages of speed and stability that can’t always be achieved with SaaS. We are now seeing a slight movement to applications that are hybrids – combining the best of both approaches.

In healthcare, there is no clear preferred technology stack. It is all over the place. We have worked in .NET, HTML5, Java, PHP, Native Mobile Apps (iOS, Android), Python, C++, Foxpro, VB, Mirth. Cobol, MUMPS and many more. Healthcare IT has traditionally seen a very fragmented approach. Chetu has the great advantage of being agnostic. We can and will work with nearly any platform or tool.

EMR usability (or lack thereof) has been a major topic of discussion. How do you manage this with your EHR clients?

We have had the opportunity to work with dozens of different EMRs; ambulatory and hospital based. Many of these EMRs are the product of individual physicians or physician groups that are unhappy with their current EMR and have not seen any existing EMRs that meet their usability needs. They have come to us with their ideas about developing an EMR from scratch. We have developed ENT, Ophthalmology, Plastic Surgery and other specialty focused EMRs stemming from this issue.

What are you seeing happening with mobile in healthcare?

There is a tremendous rush to mobile in Healthcare right now. Over the past several years our Healthcare mobile development has grown tenfold. There are many, many great mobile applications developed with patients, physicians, nurses, home health providers and others in mind. These apps have been and will continue to make providers, payers and patients lives easier and make delivering healthcare more efficient and productive.

You’ve worked with a lot of the various healthcare standards. How do they compare to the standards you work with in other industries?

There really is no parallel to the standards that guide healthcare in other industries. From my limited experience I would say that the Banking/Financial industry comes closest. But even then the amount and complexity of the standards are a fraction of what is found in Healthcare and Pharma.

Tell us about some of your work on the major hospital platforms like Siemens Soarian, Meditech and Epic. Is it a challenge working with these large companies?

These large companies have invested millions of dollars building and improving the very complex systems. So, they are rightfully concerned and selective about how and who is allowed to work in their systems. It can be a challenge, but not impossible to work with these companies. An added challenge comes from the hospitals themselves. There is the attitude that these systems are so unique that only company trained personnel have the capability to work in them.

Chetu, having worked in the Soarian, Meditech, Epic, Cerner, McKesson and other hospital platforms understands that the underlying technology in all of these systems are the same or very similar. Although each system may have unique capabilities – we recognize that the goal is the same for each. And, in getting past the UI or getting “under the hood” so to speak, we see mostly the same technologies at work.

What are the most innovative healthcare IT projects you see out there that you like working on?

Right now we are seeing a rush to capitalize on the tremendous amount of data that EMRs are generating. Data analytics using this great resource is helping pharmaceutical companies, scientists and researchers, Accountable Care Organizations – nearly everyone on the healthcare continuum provide better and less expensive patient care. This is an area that is in its infancy but we see growing rapidly.

What types of data analytics projects have you done in healthcare? Do you do just the programming component or can you do every part of a data analytics project?

Chetu has been involved in numerous healthcare analytics projects. We have helped our customers with data warehousing, data mining, OLAP, business analysis, automated report generation, multi-dimensional information “cubes”, custom reporting solutions using tools like Informatica, DTS / SSIS, Datastage and SSRS, SSAS, Cognos, Microstrategy, Crystal, OBIEE.

We have developed solutions across the complete data analytics process. From data mining and ETL to data cube and data modeling and report generation we have the experience and the people that can handle nearly any healthcare analytics project.

Full Disclosure: Chetu is an advertiser on EMR and HIPAA.

Analytics-Driven Compassionate Healthcare at El Camino Hospital

Written by: Mandi Bishop

Given its location in the heart of Silicon Valley, it may not be remarkable that El Camino Hospital was the first hospital in the US to implement EMR. What IS remarkable is that El Camino implemented EMR 51 years ago, leveraging an IBM mainframe system that Lockheed Martin refactored for healthcare from its original intended use for the space program.

Take a moment to process that. El Camino didn’t need PPACA, Meaningful Use, HITECH, or HIPAA to tell them health data is critical. El Camino saw the value in investing in healthcare IT for electronic data capture and communication without federal incentive programs or lobbyists. With that kind of track record of visionary leadership, it’s no wonder they became early analytics program adopters, and recently turned to Health Care DataWorks (HCD) as a trusted partner.

When I sat down with executive leadership from El Camino and HCD to discuss the journey up Tom Davenport‘s analytics maturity scale from rudimentary operational reporting to advanced analytics, I expected a familiar story of cost pressure, clinical informatics, quality measure incentives or alternative payment models as the business drivers for new insights development. Instead, I heard the burgeoning plan for a visionary approach to patient engagement and “analytics-driven compassionate care”.

Greg Walton, CIO of El Camino Hospital, admitted that initial efforts to implement an analytics program had resulted in “textbook errors”: “’Competing on Analytics’ was easier to write than execute,” he said. Their early efforts to adopt and conform to a commercially-available data model were hindered by the complexity of the solution and the philosophy of the vendor. “One of the messages I would give to anybody is: do NOT attempt this at home,” Greg laughed, and El Camino decided to change their approach. They sought a “different type of company…a real-life company with applicable lessons learned in this space.”

“The most important thing to remember in this sector: you’re investing in PEOPLE. This is a PEOPLE business,” Greg said. “And that if there’s any aspect of IT that’s the most people-oriented, it’s analytics. You have to triangulate between how much can the organization absorb, and how fast they can absorb it.” In HCD, El Camino found an analytics organization partner whose leadership and resources understand healthcare challenges first, and technology second.

To address El Camino’s need for aggregated data access across multiple operational systems, HCD is implementing their pioneering KnowledgeEdge Enterprise Data Warehouse solution,including its enterprise data model, analytic dashboards, applications and reports. HCD’s technology, implementation process, and culture is rooted in their deep clinical and provider industry expertise.

“The people (at HCD) have all worked in hospitals, and many still work there occasionally. Laypersons do not have the same understanding; HCD’s exposure to the healthcare provider environment and their level of experience provides a differentiator,” Greg explained. HCD impressed with their willingness to roll up their sleeves and work with the hospital stakeholders to address macro and micro program issues, from driving the evaluation and prioritization of analytics projects to identifying the business rules defining discharge destination. And both the programmers and staff are “thrilled,” Greg says: “My programmers are so happy, they think they’ve died and gone to heaven!”

This collaborative approach to adopting analytics as a catalyst for organizational and cultural change has lit a fire to address the plight of the patient using data as a critical tool. Greg expounded upon his vision to achieve what Aggie Haslup, Vice President of Marketing for HCD, termed “analytics-driven compassionate care”:

We need to change the culture about data without losing, and in fact enhancing, our culture around compassion. People get into healthcare because they’re passionate about compassion. Data can help us be more compassionate. US Healthcare Satisfaction scores have been basically flat over the last 10 years. Lots of organizations have tried to adopt other service industry tools: LEAN,6S; none of those address the plight of the patient. We’ve got to learn that we have to go back to our roots of compassion. We need to get back to the patient, which means “one who suffers in pain.” We want (to use data) to help understand more about person who’s suffering. My (recent) revelation: what do you do w/ guests in your house? Clean the house, put away the pets, get food, do everything you can to make guests comfortable. We want to know more about patients’ ethnicity, cultural heritage, the CONTEXT of their lives because when you’re in pain, what do you fall back on? Cultural values. We want a holistic view of the patient, because we can provide better, compassionate care through knowing more about patients. We want to deploy a contextual longitudinal view of the patient…and detect trends in satisfaction with demographics, clinical, medical data.

What a concept. Imagine the possibilities when a progressive healthcare provider teams with an innovative analytics provider to harness the power of data to better serve the patient population. I will definitely keep my eye on this pairing!

ACOs Want Advanced Analytics, Data Warehousing, But Are They Ready?

Written by: Katherine Rourke

ACOs are gunning to acquire advanced analytics tools and data warehousing capabilities, according to a report in iHealthBeat. This conclusion comes from a new report from IDC Health Insights, which did a May 2012 survey of 40 hospitals and health insurance companies plus interviews with vendors and industry talking heads.

As part of the survey, IDC asked about ACOs’ top investment priorities, and found 50 percent most want advanced analytics capabilities, while 46 percent cited data warehousing.

The report also noted that ACO-involved entities are picking up analytics capabilities by acquiring infrastructure and software, as well as bringing informatics and data analysis experts on staff.

When asked what kind of information they’d like to review using analytics, they stated the following, according to iHealthBeat:

73% of survey respondents cited clinical structured data
70% cited care management data
57% cited claims data
42% cited data from mobile devices
32% cited data from social media sources
29% cited unstructured clinical data

And when asked what functions they’d put the analytics data to use on, they responded as follows, iHealthBeat said:

66% of survey respondents cited identifying at-risk patients
64% cited tracking clinical outcomes
57% cited clinical decision-making at the point of care

All that being said, it’s not clear that the ACO participants know how to put these visions into action, argues John Moore of Chilmark Research. In a post-HIMSS wrap-up, Moore argues that the market for healthcare analytics tools is “hyped beyond imagination,” and that beyond the hype, many providers are actually clueless as to what they want from analytics.

At HIMSS, he says, he found a “very immature” buyers’ market in which providers aren’t even sure what they’re asking for in analytics, or why they need these tools in the first place. In fact, Moore notes, he talked to many vendors who have stopped responding to “horrible” RFPs which suggest that institutions aren’t at all ready to pursue an analytics solution.

This wouldn’t be the first time that the hype factor exceeded the industry’s actual understanding of a product or technology. But buying analytics tools before you have a clue how to use them is a particularly serious financial and strategic mistake, wouldn’t you say?

HIMSS Analytics Clinical & BI Maturity Model

Written by: Mandi Bishop

While the theme of HIMSS 2013 may have been, “How Great Is Interoperability,” the effectiveness of the many facets of interoperability are only as good as the actionable value of the shared data. The clinical insights that should be enabled by Meaningful Use Stage 2+ are expected to drive market trends in myriad areas of the healthcare system: chronic disease management, targeted member interventions, quality measures. In order to assess organizational readiness to capitalize on the promise of Meaningful Use, HIMSS Analytics began measuring the implementation and adoption of EMR and clinical documentation using a maturity model called EMRAM.

But, in analytics terms, EMRAM’s results are simply targeted foundational reporting, answering the question, “WHAT happened with Meaningful Use EMR adoption criteria.” So, you’ve got your clinical data in an EMR. Now what are you able to DO with it?

In 2013, HIMSS Analytics is taking a broader approach with the introduction of a new Clinical Business Intelligence maturity model, creating a framework to benchmark participating providers’ analytics maturity level.

I’ve been fortunate to know James Gaston, Senior Director of HIMSS Analytics Clinical & Business Intelligence, for many years, going back to his days with Arkansas Blue Cross. His appreciation for BI initiatives is matched only by his enthusiasm for the first day of turkey hunting season. When I ran into him at TDWI’s BI World summit in Orlando in November, he acted like a kid on Christmas morning, telling me about the brave new world of clinical data management that he was about to tackle. The excitement continued to build in the months leading up to HIMSS. James was practically glowing when we spoke about the upcoming C&BI Maturity Model release.

“Our customers are interested in not just understanding how to deploy IT applications, but how effectively they’re using those applications to support clinical business intelligence, as well as analytical pursuits,” James said. “So, HIMSS Analytics partnered with IIA to create and present a Clinical & BI Maturity Model that helps healthcare organizations measure that level of effectiveness.”

Sarah Gates, the VP of Research for IIA (the International Institute of Analytics), elaborated. “The HIMSS Analytics C&BI Maturity Model leverages the Competing on Analytics DELTA model, developed by Tom Davenport, which measures not only how well you’re using data and technology, but how well you’re building an analytical organization.” There are 5 core competency measurements in the DELTA model that will inform the HIMSS Analytics C&BI analysis: Data, Enterprise, Leadership, Targets, and Analysts. The methodology is holistic, touching on the cultural aspects of the organization as well as the technical, allowing a longitudinal view of the organization’s analytics program. A yardstick value from 1-5 will be assigned to each respondent based on Davenport’s criteria for each core competency.

Although HIMSS Analytics will eventually offer Level 1-5 certification program for those organizations with observed results for analytics, James and Sarah agreed that it is not appropriate for every provider to reach for the Level 5 gold star. Per Sarah, “Healthcare is an industry just starting to discover analytics. We’re expecting to see lots of practitioners that are emerging in use of analytics, so we believe it (survey results) will be heavy on the lower end of the maturity scale. Data warehouse capabilities and staffing career paths for data analysts will be key differentiators for mature programs.” Not all providers have the resources – financial, human, and/or technical – to attain advanced analytics nirvana, and James wants to insure that these providers don’t feel as if they’ve “failed”; the goal is to baseline against the peer group, identify opportunities for improvement, and focus on what is possible for each individual organization, working within their constraints.

What can we expect to see at next year’s C&BI survey results presentation? James said, “We want to be able to talk about benchmarking the industry as a whole, helping healthcare find its way with clinical business intelligence and begin to understand how important it is, and where opportunities lie Everyone’s talking about clinical and BI – it is the opportunity to realize savings in healthcare, to use information to empower people to make better decisions.”

So, it’s up to you, providers and technology partners. You’ve implemented your EMR, achieved a high adoption rate across your organization’s core clinical processes, attested to Meaningful Use Stage 2, achieved Stage 7 on the HIMSS EMRAM scale, perhaps even participated in multi-HIE CCD medical records sharing with other provider networks. You’ve got the data in-house and availabe. It’s time to see how ready you are to rise to the analytics challenge and maximize your return on those EMR and HIE investments.

Attempt to beat your previous Doug Fridsma long jump.

Note: for the complete HIMSS 2013 Leadership Survey Results, please download PDF here.

Post a Comment(0)

March 14, 2013 I Written By Mandi Bishop

Tags:Advanced Analytics Clinical Analytics EHR Analytics EHR Interoperability HIMSS Analytics ONC ONCHIT

Oracle Brings Health Data Analytics To The Cloud

Written by: Katherine Rourke

For years now, healthcare providers have been inching toward cloud use, with CIOs still divided as to whether cloud applications are secure enough to meet their standards.

These days, though, the tide seems to be turning in favor of cloud applications. In fact, a recent study by KLAS on hybrid clouds in healthcare found that those who had signed on for cloud apps rated them a 4.5 out of 5 for security.

Given this growing level of trust, it was no surprise to read that Oracle had kicked off a major cloud product for healthcare at HIMSS last week.

At the show, Oracle Health Sciences introduced the Oracle Enterprise Healthcare Analytics Cloud Service, a cloud-based version of the vendor’s data management, warehousing and analytics platform. The new product comes with pre-built analytical applications and also supports third-party healthcare apps.

The existing Enterprise Healthcare Analytics is a big data play which pulls in, validates and loads data from clinical, financial, administrative and even clinical research systems to offer a single enterprise view.

What makes the cloud version interesting, of course, is that if healthcare CIOs are willing to chance the security issues, they can bypass having to spend big on IT infrastructure to bring it on board.

Also interesting is that Oracle has also given CIOs a few models to deploy Enterprise Healthcare Analytics available to be deployed” on-site in its “HIPAA-certified” Oracle Health Sciences Cloud, or in a hybrid model leveraging on-premise and traditional cloud.

I have little doubt that even as a cloud-based service, this is a very pricey product that isn’t for all facilities. And there’s still a large contingent of hospitals that aren’t ready to trust all of their mission-critical data to cloud security.

But it’s still worth note to see Oracle extending this kind of tool to the cloud nonetheless. I wonder if the perceived value of an Oracle app will push more facilities off the fence and into trusting cloud security after all?

Post a Comment(0)

March 12, 2013 I Written By Katherine Rourke

Filed Under: EHR Electronic Health Record Electronic Medical Record EMR EMR Security Healthcare Healthcare Business Intelligence HealthCare IT HIPAA General Hospital EHR Hospitals

Tags:Big Data Healthcare Data Warehouse Oracle Enterprise Healthcare Analytics Cloud Service

What Would ONC’s Dr. Doug Fridsma Do? (THIS Geek Girl’s Guide to HIMSS)

Written by: Mandi Bishop

I know you’ve all been wondering how I’m planning to spend my mad crazy week at HIMSS in New Orleans. Well, maybe not ALL of you, but perhaps at least one – who is most likely my blog boss, the master John Lynn. Given the array of exciting developments in healthcare IT across the spectrum, from mobile and telehealth to wearable vital sign monitoring devices, EMR consolidation to cloud-based analytics platforms, it’s been extraordinarily difficult to keep myself from acting like Dori in “Finding Nemo”: “Oooooh! Shiny!” I’ve had to remind myself daily that I will have an opportunity to play with everything that catches my eye, but that I am only qualified to write and speak intelligently on my particular areas of expertise. And so, I’m proud to say I’ve finally solidified my agenda for the entire week, and I cannot WAIT to go ubergeek fan girl on so many industry luminaries and fascinating up-and-comers making great strides towards interoperability, deriving the “meaning” in “Meaningful Use” from clinical data, and leveraging the power of big data analytics to improve quality of patient experience and outcomes.

On Sunday, I’m setting the stage for the rest of the week with a sit-down with ONC’s Director of Standards and Interoperability and Acting Chief Scientist, Dr. Doug Fridsma. His groundbreaking work in interoperability spans multiple initiatives, including: the Nationwide Health Information Network (NwHIN) and the CONNECT project, as well as the Federal Health Architecture. For insight into his passion for transforming the healthcare system through health IT, check out his blog: From The Desk of the Chief Science Officer.

Through the rest of the week, I aspire to see the world through Dr. Fridsma’s eyes, focusing on how each of the organizations and individuals contribute to the standards-based processes and policies that form the foundation for actionable analytics – and improved health. I’ve selected interviews with key visionaries from companies large and small, who I feel are representative of positive forward movement:

Health Care DataWorks piques my interest as an up-and-comer to watch, empowering healthcare systems to improve outcomes and reduce medical costs by providing accelerated EDW design and implementation, whether on-premise or via SaaS solution. Embedded industry analytics models supporting alternative network models, population-based payment models, and value-based purchasing allow for rapid realization of positive ROI.

Emdeon, is the single largest clinical, financial, and administrative network, connecting over 400,000 providers and executing more than seven billion health exchanges annually. And if that’s not enough to attract keen attention, they recently announced a partnership with Atigeo to provide intelligent analytics solutions with Emdeon’s PETABYTES of data.

Serving an area near and dear to my heart, Clinovations provides healthcare management consulting services to stakeholders at each link in the chain, from providers to payers and supporting trading partners – in areas from EMR implementation (and requisite clinical data standards) to market and vendor assessments, and data management activities throughout. With the dearth in qualified SME resources in the clinical data field, I look forward to learning about how Clinovations plans to manage their growth and retain key talent.

Who doesn’t love a great legacy decommissioning story? Mediquant proports adopting their DataArk product can result in an 80% reduction in legacy system costs through increased interoperability across disparate source systems and consolidated access. The “active archiving” solution allows for a centralized repository and consolidated accounting functions out of legacy data without continuing to operate (and support) the legacy system. Longitudinal clinical records? Yes, please!

Those are just a few on my must-see list, and I think Dr. Doug Fridsma would be proud of their vision, and find alignment to his ONC program goals. But will he be proud of their execution?

Can’t wait to find out, on the exhibit hall floor – and in the hallway conversations, and the client case study sessions, and the general scuttlebutt – at HIMSS!

Post a Comment(0)

March 2, 2013 I Written By Mandi Bishop

Filed Under: Certified EHR Clinical Decision Support Direct Project EHR Electronic Health Record Electronic Medical Record EMR Healthcare Healthcare Business Intelligence Healthcare Interoperability HealthCare IT Healthcare Social Media HIE HITECH HL7 Interfaces Meaningful Use

Tags:Advanced Interoperability Health Information Exchange Healthcare Data Interoperability HealthCare IT National Health Information Network Nationwide Health Information Network ONC ONCHIT

Visualization of Healthcare Data, DocGraph, and Open Source — #HITsm Chat Highlights

Written by: Katie

Topic One: How can we leverage referral and collaboration information in #HealthIT software? What is DocGraph good for?

T1. The interesting thing for me about Docgraph is that it doesn’t just show connection between docs, but flow/directionality of pts. #hitsm — Leonard Kish (@leonardkish) February 8, 2013

T1: I’d like to see how many referrals go outside local regions.Is it mostly local or are we becoming more of a national system?#HITsm — EMR, EHR and HIT(@ehrandhit) February 8, 2013 T1: back to the topic: what if #healthIT graph could be an eye opener to docs on their activities? They may be unaware of activity #HITsm — Chad Johnson (@OchoTex) February 8, 2013

T1: I read the article and it would love to see the referral traffic map, see how it benefits certain areas of a city. City vs. rural #HITsm — Chad Johnson (@OchoTex) February 8, 2013

T1: Tied to clinical patient experience data. Does Doc A continue to refer to Doc B if survey was negative? #HITsm — Loran Cook (@loranstefani) February 8, 2013

Topic Two: Generally, what are the best examples of data visualization of healthcare data that you have seen or heard of?

T2: Do the best examples have to be in healthcare? If so I imagine public health sector has got the market on that #HITsm — Chad Johnson (@OchoTex) February 8, 2013

T2: Lab results and patient friendly reporting for “patient access” are the best examples Ive seen recently…. #HITsm — Linda Lia (@EMRAnswers) February 8, 2013

T2: Trending is critical in acute care ~ Nihon Kohden & others monitor, VS plus SpO2 w/trending into EMR to see when pt is declining #HITsm — Melissa Cole, FACHE (@ColeFACHE) February 8, 2013

Topic Three: What other open doctor data should we merge with DocGraph? #HealthIT

T3: When the Sunshine Act is fully implemented in Sept 2014, pharma payments would be interesting to see tied with referral data. #HITsm

— Loran Cook (@loranstefani) February 8, 2013

T3Are referrals influenced by payor class, and do IDN look different pattern-wise than volume driven silos? #HITsm

— ACO Alliance (@acoalliance) February 8, 2013

T3: Medical necessity / cost / waste is huge as is patient safety value #HITsm

— Linda Lia (@EMRAnswers) February 8, 2013

Topic Four: What open data or open source software do you use regularly as a #HIT professional? #HealthIT

T4: Quality/cost report data are the most frequent; would include census and NPI datasets but I avoid them like the plague #messydata #HITsm

— Loran Cook (@loranstefani) February 8, 2013

T4: there has been a lot of talk about open source #EHRs, like OpenMRS, this week.#HITsm

— Chad Johnson (@OchoTex) February 8, 2013

Topic Five: What open data or open source software do you wish existed? #HealthIT

T5: I wish there was an open source maker movement on drugs. Imagine cost savings if u could “make” personalized drug by DL one #HITsm

— Colin Hung (@Colin_Hung) February 8, 2013

T5: For quality, I would like to see statewide physician credentials, state medical board reporting to be open/free. #HITsm

— Chad Johnson (@OchoTex) February 8, 2013

Post a Comment(0)

February 9, 2013 I Written By Katie

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

Filed Under: Healthcare Healthcare Business Intelligence HealthCare IT

Tags:#HITSM #HITsm Chat DocGraph Fred Trotter Healthcare Data Opensource

Health Data: Little White Lie Detector

Written by: Mandi Bishop

As we bring 2012 to a close and ponder the new year ahead, many of us make resolutions to change something in our lives, and frequently, that something is our health. According to the University of Scranton Journal of Psychology, 47% of Americans make New Years Resolutions. Of those, the #1 New Years Resolution for 2012 is to lose weight. Staying fit and healthy and quitting smoking also appear in the top 10. Each of these health-related resolutions translates into quantifiable healthcare data that is, or can be, captured and measured to assist the resolution-makers in achieving their goals. Our calorie consumption and burn can be calculated, our blood oxygen level monitored, our ratio of fat:lean muscle mass tracked over time. If only we were all a bit more like George Washington, and couldn’t tell a lie, the success rate for annual resolutions would be higher than 8%.

The inclination to tell little white lies to protect ourselves from inconvenient, uncomfortable truths exists in all of us. “Do these jeans make my butt look fat,” meets, “Of course not,” rather than, “Yes, your butt DOES look fat in those jeans – but it’s not the jeans’ fault.” “Can Timmy come play,” warrants, “We already have plans – let’s rain check,” in lieu of, “Your child is a brat who cannot enter my home because I prefer to keep all my hair rooted in my scalp.”

Many, if not most, of us extend these white lies to ourselves. The dress that fit last month but doesn’t today “shrunk at the dry cleaner”. Cigarettes only smoked during cocktail hour don’t really count as “smoking”. You count the time you spend standing to give office presentations as “exercise”. You “usually” eat healthy, except for the tell-tale McDonald’s bags in your garbage showing a once-a-day burger and fries habit.

What if there were a way to identify and hold you accountable for these self-delusions – a health data lie detector? Would you change your behavior? Could you achieve your healthy resolution? And might it have a quantifiable impact on healthcare cost if you did?

I had a partial thyroidectomy a few years ago. A year after my surgery, I found I had gained 7 pounds in 11 days, was feeling lethargic and was having difficulty sleeping. As a very active adult who meticulously maintained body weight for a decade, I was disturbed, and convinced that my symptoms were a result of my remaining thyroid tissue failing. I went to my primary care physician to request a hormone test.

The nurse and doctor both agreed that, in 90% of cases, the root cause of weight gain is diet, and they asked myriad questions, capturing all my answers in the clinical notes of their EMR: had I been eating differently, had I altered my exercise routine, had I been traveling. I was adamant that nothing had drastically changed. Given my fitness and history, they agreed to order the hormone test, and a blood vitamin test, as well.

All lab work came back normal. BETTER than normal. So I retraced every detail of my routine over those 11 days. And I discovered the culprit: office candy.

A bad meeting one day led to grabbing a handful of chocolates from one co-workers bowl, which became grabbing a handful of chocolates from each bowl I encountered on my department’s floor…several times a day. Did you know there are 35 calories in a single Hershey’s kiss? 220 calories in a handful of peanut M&Ms? 96 calories in a mini-Butterfinger bar? Turns out, I was eating between 500-700 calories a day in office candy. And that wasn’t all.

Along with the chocolate snacks, I’d fallen into some poor nutrition habits at meals. I started to consume other starchy carbs regularly: the pre-dinner bread basket at restaurants, pizza, pasta, sandwich bread. I didn’t feel I ate to excess, but I also didn’t take into account the difference in nutrient density between the mass quantities of fruits and vegetables I had been eating for years, and the smaller (yet still plentiful) quantities of processed starches I was currently eating.

The changes in diet likely disturbed my sleeping pattern and led to my lethargy, which in turn made my daily workouts less intense and effective at calorie-burning.

In short, my weight gain was legit, and the two doctor visits and the lab tests could have been avoided had I been completely honest with myself. I cost each actor in the healthcare system money with my self-deluding little white lie: the office administrative staff, the LRNP, the doctor, the medical coder, the lab, the insurance company, myself. There is also a per-transaction cost associated with each HIPAA-covered request that the doctors’ office EMR and lab information system generated. Given that I have only been to the doctor three times this year, and twice was for this weight gain concern, one could accurately conclude that 66% of my annual medical costs could have been avoided in 2012.

The health data exists within Meaningful Use-certified EMR systems to capture and communicate both the absolute data (height, weight, lab results, etc.) and the unstructured notes data (patient comments, doctor notes, responses to questionnaires, etc.). The capability to automatically compare the absolute with the unstructured data already exists. It wouldn’t take an inordinate amount of effort to program a lie detector to call out many of the most common little white lies.

What would happen to medical cost if we stopped lying to ourselves, and to our healthcare providers? And how high a percentage of the nation’s total healthcare bill could be avoided by this type of analysis? Better still, how much would the healthcare industry change if patients not only took responsibility for their own action/inaction, but modified their behaviors accordingly?

I’ll tell you what happened to me. I dropped the candy and starchy carbs, and I lost those 7 pounds. Keeping them off will be 2013′s New Years Resolution.

M	T	W	T	F	S	S
« Apr
		1	2	3	4	5
6	7	8	9	10	11	12
13	14	15	16	17	18	19
20	21	22	23	24	25	26
27	28	29	30	31

What’s Ahead After TEDMED 2013

How Do You Improve the Quality of EHR Data for Healthcare Analytics?

EMR and Health IT Development – Interview with Chetu

Analytics-Driven Compassionate Healthcare at El Camino Hospital

ACOs Want Advanced Analytics, Data Warehousing, But Are They Ready?

HIMSS Analytics Clinical & BI Maturity Model

Oracle Brings Health Data Analytics To The Cloud

Visualization of Healthcare Data, DocGraph, and Open Source — #HITsm Chat Highlights

Top EMR & HIPAA Posts

Calendar

Recent Posts

EMR & HIPAA Resources

Recent Comments

Categories