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From Fragmented to Coordinated: The Big Data Challenge

Posted on November 27, 2018 I Written By

The following is a guest blog post by Patty Sheridan, MBA, RHIA, FAHIMA; SVP, Life Sciences at Ciox.

When healthcare organizations have access to as much data as possible, that translates into improved coordination and quality of care, reduced costs for patients, payers and providers, and more efficient medical care. Yet, there is a void in the healthcare data landscape when it comes to securing the right information to make the right decision at the right time. It is becoming increasingly critical to ensure that providers understand data and are able to properly utilize it. Technologies are emerging today that can help deliver a full picture of a patient’s health data, which can lead to more consistent care and the development of improved therapies by helping providers derive better insights from clinical data.

Across the country, patient data resides across multiple systems, and in a variety of structured and unstructured formats. The lack of interoperability makes it difficult for organizations to have access to the data they need to run programs that are critical to patient care. Often, various departments within an organization seek the same information and request it separately and repeatedly, leading to a fragmented picture of a patient’s health status.

Managing Complexity, Inside and Out

While analytics tools work well within select facilities and research communities, these vast data sets and the useful information within them are very complex, especially when combined with data sets from outside organizations. The current state of data illiquidity even makes it challenging to seamlessly share and use data within an organization.

For example, in the life sciences arena, disease staging is often the foundation needed to identify a sample of patients and to link to other relevant data which is then abstracted and mined for real world use; yet clinical and patient reported data is rarely documented in a consistent manner in EHRs. Not only do providers often equivocate and contradict their own documentation, but EHR conventions also promote errors in the documentation of diagnostic findings. Much of the documentation can be found in unstructured EHR notes that require a combination of abstraction and clinician review to determine the data’s relevance.

Improved Interoperability, Improved Outcomes

Problems with EHR interoperability continue to obstruct care coordination, health data exchange and clinical efficiency. EHRs are designed and developed to support patient care delivery but, in today’s world of value-based care, the current state of EHR interoperability is insufficient at best.

Consider the difficulty in collecting a broad medical data set. The three largest EHRs combined still corner less than one-third of the market, and there are hundreds of active EHR vendors across the healthcare landscape, each bringing its own unique approach to the information transfer equation. Because many hospitals use more than one EHR, tracking down records for a single patient at a single hospital often requires connecting to multiple systems. To collect a broader population data set would require ubiquitous connection to all of the hundreds of EHR vendors across the country.

The quality integration of health data systems is essential for patients with chronic conditions, for example. Patients with more serious illnesses often require engagement with several specialists, which means it is particularly important that the findings and data from each specialist are succinctly and properly communicated to fellow doctors and care providers.

Leveraging Technology

As the industry matures in its use of data, emerging technologies are beginning to break down information road blocks. Retrieving, digitizing and delivering medical records is a complex endeavor, and technology must be layered within all operations to streamline data acquisition and make executable data available at scale, securing population-level data more quickly and affordably.

When planning to take advantage of new advanced technologies, seek a vendor partner that provides a mix of traditional and emerging technologies, including robotic process automation (RPA), computer vision, natural language processing (NLP) and machine learning. All of these technologies serve vital functions:

  • RPA can be used to streamline manually intensive and repetitive systematic tasks, increasing the speed and quality at which clinical and administrative data are retrieved from the various end-point EHRs and specialty systems.
  • NLP and neural networks can analyze the large volume of images and text received to extract, organize and provide context to coded content, dealing with ambiguous data and packaging the information in an agreed-upon standard.
  • With machine learning, an augmented workforce can be equipped to increase the quality of records digitization and the continuous learning across the ecosystem, where every touchpoint is a learning opportunity.

Smarter, faster and more qualitative systems of information exchange will soon be the catalysts that lead paradigm-shifting improvements in the U.S. care ecosystem, such as:

  • Arming doctors with relevant information about patients
  • Increasing claims accuracy and accelerating providers’ payments
  • Empowering universities and research organizations with timely, accurate and clinically relevant data sets
  • Correlating epidemics with the preparedness of field teams
  • Alerting pharmacists with counter-interaction warnings

Ultimately, improving information exchange will enable healthcare industry professionals to elevate patient safety and quality, reduce medical and coding errors tenfold and enhance operational efficiencies by providing the relevant data needed to quickly define treatment.

Achieving this paradigm shift depends almost entirely on taking the necessary steps to adopt these emerging technologies and drive a systematic redesign of many of our operations and systems. Only then will we access the insights necessary to truly impact the quality of care across the healthcare landscape.

About Ciox
Ciox, a health technology company and proud sponsor of Healthcare Scene, is dedicated to significantly improving U.S. health outcomes by transforming clinical data into actionable insights. Combined with an unmatched network offering ubiquitous access to healthcare data, Ciox’s expertise, relationships, technology and scale allow for the extraction of insights from structured and unstructured clinical data to create value for healthcare stakeholders. Through its HealthSource technology platform, which includes solutions for data acquisition, release of information, clinical coding, data abstraction, and analytics, Ciox helps clients securely and consistently solve the last mile challenges in clinical interoperability. Ciox improves data management and sharing by modernizing workflows and increasing the accuracy and flow of information, while providing transparency across the healthcare ecosystem and helping clients manage disparate medical records. Learn more at www.ciox.com.

Clinicians Say They Need Specialized IT To Improve Patient Safety

Posted on July 24, 2018 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.

Hospitals are loaded down with the latest in health IT and have the bills to prove it. But according to a new survey, they need to invest in specialized technologies to meet patient safety goals, as well as providing more resources and greater organizational focus.

Health Catalyst recently conducted a national survey of physicians, nurses and health executives to gather their thoughts on patient safety issues. Among its main findings was that almost 90% of respondents said that their organizations were seeing success in improving patient safety. However, about the same percentage said there was room for improving patient safety in their organization.

The top obstacle they cited as holding them back from the patient safety goals was having effective information technology, as identified by 30% of respondents. The same number named a lack of technologies offering real-time warnings of possible patient harm.

These were followed by lack of staffing and budget resources (27%), organizational structure, culture priorities (19%), a lack of reimbursement for safety initiatives (10%) and changes in patient population practice setting (9%).

Part of the reason clinicians aren’t getting as much as they’d like from health IT is that many healthcare organizations rely largely on manual methods to track and report safety events.

The top sources of data for patient safety initiatives respondents used for safety initiatives voluntary reporting (82%). Hospital-acquired infection surveys (67%), manual audits (58%) and retrospective coding (29%). Such reporting is typically based on data sets which are at least 30 days old, and what’s more, collecting and analyzing the data can be time and resource-consuming.

Not surprisingly, Health Catalyst is launching new technology designed to address these problems. Its Patient Safety Monitor™ Suite: Surveillance Module uses protective and text analytics, along with concurrent critical reviews of data, to find and prevent patient safety threats before they result in harm.

The announcement also falls in line with the organization’s larger strategic plans, as Health Catalyst has applied to the AHRQ to be certified as a Patient Safety Organization.

The company said that he had spent more than $50 million to create the Surveillance module, whose technology includes the use of predictive analytics models and AI. It expects to add new AI and machine learning capabilities to its technology in the future which will be used to propose strategies to eliminate patient safety risks.

And more is on the way. Health Catalyst is working with its clients to add new features to the Suite including risk prediction, improvement tracking and decision support.

I’m not sure if it’s typical for PSOs to bringing their own specialized software to the job, but either way, it should give Health Catalyst a leg up. I have little doubt that doing better predictive analytics and offering process recommendations would be useful.

UPMC Plans $2B Investment To Build “Digitally-Based” Specialty Hospitals

Posted on November 20, 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.

The University of Pittsburgh Medical Center has announced plans to spend $2 billion to build three new specialty hospitals with a digital focus. Its plans include building the UPMC Heart and Transplant Hospital, UPMC Hillman Cancer Hospital and UPMC Vision and Rehabilitation Hospital. UPMC already runs the existing specialty hospitals, Magee-Womens Hospital, Western in Psychiatric Institute and Clinic and Children’s Hospital of Pittsburgh.

UPMC is already one of the largest integrated health delivery networks in the United States. It’s $13 billion system includes more than 25 hospitals, a 3-million-member health plan and 3,600 physicians. If its new specialty centers actually represent a new breed of digital-first hospital, and help it further dominate its region, this could only add to its already-outsized clout.

So what is a “digitally-based” hospital, and what makes it different than, say, other hospitals well along the EMR adoption curve? After all, virtually every hospital today relies on a backbone of health IT applications, manages patient clinical data in an EMR and stores and stores and shares imagines in digital form.   Some are still struggling to integrate or replace legacy technologies, while others are adopting cutting-edge platforms, but going digital is mission-critical for everyone these days.

What’s interesting about UPMC’s plans, however, is that the new hospitals will be designed as digitally-based facilities from day one. UPMC is working with Microsoft to design these “digital hospitals of the future,” building on the two entities’ existing research collaboration with Microsoft and its Azure cloud platform.

The Azure relationship dates back to February of this year, when UPMC struck a deal with Microsoft to do some joint technology research. The agreement builds on both UPMC’s fairly impressive record of tech innovation and Microsoft’s healthcare AI capabilities, genomics and machine learning capabilities. For example, in working with Microsoft, UPMC gets access to Microsoft’s health chat bot technology, which is being deployed elsewhere to help patient self-triage before they interact with the doctor for a video visit.

I’d love to offer you specific information on how these new digitally-oriented will be designed, and more importantly how the functioning will differ from otherwise-wired hospitals that didn’t start out that way, but I don’t think the two partners are ready to spill the beans. Clearly, they’re going to tell you all of this is the new hotness, but nobody’s provided me with any examples of how this will truly improve on existing models of digital hospital technology. I just don’t think they’re that far along with the project yet.

Obviously, UPMC isn’t spending $2 billion lightly, so its leadership must believe the new digital model will offer a big payoff. I hope they know something we don’t about the ROI potential for this effort. It seems likely that if nothing else, that technology investment alone won’t drive that big a rate of return. Clearly, other major factors are in play here.

Promoting Internal Innovation to Drive Healthcare Efficiency

Posted on June 1, 2017 I Written By

The following is a guest blog post by Peyman S. Zand, Partner, Pivot Point Consulting, a Vaco Company.

Technical innovation in healthcare has historically been viewed through the lens of disruption. As tech adoption in the industry matures, perceptions on the origin of innovation are evolving as well. Healthcare leadership teams are increasingly leaning on feedback from the front lines of care delivery to identify ways to eliminate waste and drive greater efficiency. Rather than leaving innovation up to third parties, many health organizations are formalizing programs to advance innovation within their own facilities.

There are two schools of thought on healthcare innovation. Some argue that the market’s unique challenges can only be understood by those in the field, leaving outside influencers destined to fail. Others view innovation success in outside markets as an opportunity for healthcare stakeholders to learn from the wins and losses of more technically progressive industries. By mimicking other industries’ approach to promoting innovation (as opposed to their byproducts) in our hospitals and health systems, healthcare can draw from the best of both worlds. What we know is that the process in which innovation is adopted is very similar in all industries. However, the types of innovations and specific models can and should be tailored to the healthcare industry.

Innovation in Healthcare: Three Examples at a  Glance

There are several examples of health organizations successfully forging a path to institutionalized innovation. University of Pittsburg Medical Center (UPMC), Intermountain Healthcare and Mayo Clinic have pioneered innovation programs that merge internal clinical expertise with technical innovators from vertical markets in and outside healthcare. This article highlights some of the ways these progressive organizations have achieved success.

Innovation at UPMC

UPMC Enterprises boasts a 200-person staff managed by top provider and payer executives at UPMC. The innovation team is presently engaged in more than a dozen commercial partnerships, including support for Vivify Health’s chronic care telehealth solutions, medCPU’s real-time decision support solutions and Health Catalyst’s data warehousing and analytics solutions. Each project is focused on the goal of improving patient outcomes. The innovation group was recently rumored to be partnering with Microsoft on machine learning initiatives and the results may have a profound impact on how we use technology in care delivery.

UPMC Enterprises supports entrepreneurs—both internal individuals and established companies—with capital, technical resources, partner networks, recruiting and marketing assistance to support innovation. Dedicated focus in the following areas lends structure to the innovation program:

  • Translational science
  • Improving outcomes
  • Infrastructure and efficiency
  • Consumer engagement

All profits generated from investments are reinvested to support further research and innovation.

Innovation at Intermountain Healthcare

Like UPMC, Intermountain’s Healthcare Transformation Lab supports innovation in the areas of telehealth and natural language processing (NLP), among others. Like most providers, one of Intermountain’s primary goals is controlling costs. The group’s self-developed NLP program is designed to help identify high-risk patients ahead of catastrophic events using data stored in free-text documents. Telehealth innovations let patients self-triage to the right level of care to incentivize use of the least expensive form of care available. Intermountain’s ProComp solution offers its providers on-the-spot transparency about the cost of instruments, drugs and devices they use. That innovation alone net the health system roughly $80 million in reduced costs between 2013 and 2015.

Most of Intermountain’s innovation initiatives are physician led or co-led. The program strives for small innovations in day-to-day work, supported by a suite of innovation support services and resource centers. Selected innovations from outside startups are supported by the company’s Healthbox Accelerator program involvement, while internal innovations are managed by the Intermountain Foundry. Intermountain offers online innovation idea submissions to promote easy participation. The health organization’s $35 million Innovation Fund supports innovations through formalized investment criteria and trustee governance resources. It is important to note that Intermountain Healthcare is interested in all aspects of innovation including supply chain and other non-clinical related projects.

Innovation at Mayo Clinic

Mayo Clinic’s Center for Innovation (CFI) brings in innovation best practices from both healthcare and non-healthcare backgrounds to drive new ideas. The innovation team’s external advisory council is comprised of both designers and physicians to drive innovation and efficiency in care delivery. The CFI features a Multidisciplinary Design Clinic that invites patients into the innovation process as well.

CFI staff found it was essential to show physicians data that demonstrated known problems and how proposed innovations could make a difference to their patients. They emphasize temporary changes, or “rapid prototyping,” to garner physician buy-in. Mayo’s CFI promotes employee involvement in innovative design through its Culture & Competency of Innovation platform, which features weekly meetings, institution-wide classes, lunch discussion groups and an annual symposium. Mayo’s innovation efforts include these additional physician-led platforms:

  • Mayo Clinic Connection—supporting shared physician experience
  • Prediction and Prevention
  • Wellness—promoting patient education
  • Destination Mayo Clinic—focused on improving patient experience

While these innovation examples represent large healthcare organizations, fostering innovation does not require a big budget. Mayo Clinic’s “think big, start small, move fast” approach to innovation illustrates a common thread among successful innovation programs. Here are practical strategies to advance innovation in healthcare, regardless of organizational size or budget.

Four Steps to Implementing an Innovation Program in Your Organization

Innovation doesn’t have to be grandiose or expensive. Organizations can start small. Begin by opening a companywide dialogue on innovation and launching a simple, online idea submission process to engage personnel in your organization. The most important part of this process is educating your teams to understand how to evaluate new innovations against a relatively pre-defined set of criteria.  For example, are you trying to improve patient safety, quality of care, reduce cost, increase patient or physician satisfaction, etc.

Another key element of successful innovation is encouraging collaboration and participation across a wide variety of stakeholders. Cross-functional teams bring multifaceted perspectives to the problem-solving process. Strive for incremental gains in facilitating opportunities for cross-department collaboration in your organization. This is particularly important for the implementation step.

Measure success using performance metrics where clinical efficiencies are concerned. Physician satisfaction, while difficult to quantify, can also pose big wins. You can expect some failures, but stack the odds by learning from other departments, organizations and industries to avoid making the same mistakes.

To work, innovation must happen often and organically. Dedicate funding, establish cross-department teams and build a formal process for vetting internal ideas. Consider offering staff incentives to drive engagement. Not all ideas will succeed. Identify metrics that will help determine ROI (not all ROIs are measured in dollars) on pilot programs so you can weed out initiatives that aren’t delivering early on to protect resources. Also, keep in mind that you can improve these innovations at each iteration.  Make the process iterative and roll out the initiatives quickly. If it fails, shut the process down quickly and move on. If it is successful, improve it for the next iteration and scale it quickly to maximize the benefits.

Whether you’re cross-pollinating internal teams to promote innovation, building partnerships with other organizations or leveraging technology to better connect providers and patients, healthcare’s ability to successfully collaborate is vital to advancing innovation in healthcare.

About Peyman S. Zand
Peyman S. Zand is a Partner at Pivot Point Consulting, a Vaco company, where he is responsible for strategic services solving healthcare clients’ complex challenges. Currently serving as interim regional CIO for Tenet Healthcare, Zand was previously a member of the University of North Carolina Healthcare System, leading Strategy, Governance, and Program/Project Management. He oversaw major initiatives including system-wide EHR implementation, regulatory programs, and physician practice rollouts. Prior to UNC, Zand formed the Applied Vision Group, a firm dedicated to assisting healthcare organizations with strategic planning, governance, and program and project management for key initiatives.

Zand holds a Bachelor’s of Science in Computational Mathematics and Engineering from Michigan State University, and a Master of Business Administration from the University of Michigan.

Searching for Disruptive Healthcare Innovation in 2017

Posted on January 17, 2017 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.

Disruptive Innovation has been the brass ring for technology companies ever since Clayton Christensen popularized the term in his seminal book The Innovator’s Dilemma in 1997. According to Christensen, disruptive innovation is:

“A process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors.”

Disruption is more likely to occur, therefore, when you have a well established market with slow-moving large incumbents who are focused on incremental improvements rather than truly innovative offerings. Using this definition, healthcare has been ripe for innovation for a number of years. But where is the AirBNB/Uber/Google of healthcare?

On a recent #hcldr tweetchat we asked what disruptive healthcare technologies might emerge in 2017. By far the most popular response was Artificial Intelligence (AI) and Machine Learning.

Personally, I’m really excited about the potential of AI applied to diagnostics and decision support. There is just no way a single person can stay up to speed on all the latest clinical research while simultaneously remembering every symptom/diagnosis from the past. I believe that one day we will all be using AI assistance to guide our care – as common as we use a GPS today to help navigate unknown roads.

Some #hcldr participants, however, were skeptical of AI.

While I don’t think @IBMWatson is on the same trajectory as Theranos, there is merit to being wary of “over-hype” when it comes to new technologies. When a shining star like Theranos falls, it can set an entire industry back and stifle innovation in an area that may warrant investment. Can you imagine seeking funding for a technology that uses small amounts of blood to detect diseases right now? Too much hype can prematurely kill innovation.

Other potentially disruptive technologies that were raised during the chat included: #telehealth, #wearables, patient generated health data (#PDHD), combining #HealthIT with consumer services and #patientengagement.

The funniest and perhaps most thoughtful tweet came from @YinkaVidal, who warned us that innovations have a window of usefulness. What was once ground-breaking can be rendered junk by the next generation.

What do you believe will be the disruptive healthcare technology to emerge in 2017?

Using NLP with Machine Learning for Predictive Analytics in Healthcare

Posted on December 12, 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 a lot of elements involved in doing predictive analytics in healthcare effectively. In most cases I’ve seen, organizations working on predictive analytics do some but not all that’s needed to really make predictive analytics as effective as possible. This was highlighted to me when I recently talked with Frank Stearns, Executive Vice President from HBI Solutions at the Digital Health Conference in NYC.

Here’s a great overview of the HBI Solutions approach to patient risk scores:

healthcare-predictive-analytics-model

This process will look familiar to most people in the predictive analytics space. You take all the patient data you can find, put it into a machine learning engine and output a patient risk score. One of the biggest trends happening with this process is the real-time nature of this process. Plus, I also love the way the patient risk score includes the attributes that influenced a patients risk score. Both of these are incredibly important when trying to make this data actionable.

However, the thing that stood out for me in HBI Solutions’ approach is the inclusion of natural language processing (NLP) in their analysis of the unstructured patient data. I’d seen NLP being used in EHR software before, but I think the implementation of NLP is even more powerful in doing predictive analytics.

In the EHR world, you have to be absolutely precise. If you’re not precise with the way you code a visit, you won’t get paid. If you’re not precise with how the diagnosis is entered into the EHR, that can have long term consequences. This has posed a real challenge for NLP since NLP is not 100% accurate. It’s gotten astoundingly good, but still has its shortcomings that require a human review when utilizing it in an EHR.

The same isn’t true when applying NLP to unstructured data when doing predictive analytics. Predictive analytics by its very nature incorporates some modicum of variation and error. It’s understood that predictive analytics could be wrong, but is an indication of risk. Certainly a failing in NLP’s recognition of certain data could throw off a predictive analytic. That’s unfortunate, but the predictive analytics aren’t relied on the same way documentation in an EHR is relied upon. So, it’s not nearly as big of a deal.

Plus, the value that’s received from applying NLP to pull out the nuggets of information that exists in the unstructured narrative sections of healthcare data is well worth that small amount of risk of the NLP being incorrect. As Frank Stearns from HBI solutions pointed out to me, the unstructured data is often where the really valuable data about a patients’ risk score exist.

I’d be interested in having HBI Solutions do a study of the whole list of findings that are often available in the unstructured data that weren’t available otherwise. However, it’s not hard to imagine a doctor documenting patient observations in the unstructured EHR narrative that they didn’t want to include as a formal diagnosis. Not the least of these are behavioral health observations that the doctor saw, observed, and documented but didn’t want to fully diagnose. NLP can pull these out of the narrative and include them in their patient risk score.

Given this perspective, it’s hard to imagine we’ll ever be able to get away from using NLP or related technology to pull out the valuable insights in the unstructured data. Plus, it’s easy to see how predictive analytics that don’t use NLP are going to be deficient when trying to use machine learning to analyze patients. What’s amazing is that HBI Solutions has been applying machine learning to healthcare for 5 years. That’s a long time, but also explains why they’ve implemented such advanced solutions like NLP in their predictive analytics solutions.