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Taming the Healthcare Compliance and Data Security Monster: How Well Are We Doing?

Posted on October 18, 2018 I Written By

The following is a guest blog post by Lance Pilkington, Vice President of Global Compliance at Liaison Technologies.

Do data breach nightmares keep you up at night?

For 229 healthcare organizations, the nightmare became a reality in 2018. As of late August, more than 6.1 million individuals were affected by 229 healthcare-related breaches, according to the Department of Health and Human Services’ HIPAA Breach Reporting Tool website – commonly call the HIPAA “wall of shame.”

Although security and privacy requirements for healthcare data have been in place for many years, the reality is that many healthcare organizations are still at risk for non-compliance with regulations and for breaches.

In fact, only 65 percent of 112 hospitals and hospital groups recently surveyed by Aberdeen, an industry analyst firm, reported compliance with 11 common regulations and frameworks for data security. According to the healthcare-specific brief – Enterprise Data in 2018: The State of Privacy and Security Compliance in Healthcare – protected health information has the highest percentage of compliance, with 85 percent of participants reporting full compliance, and the lowest compliance rates were reported for ISO 27001 and the General Data Protection Regulation at 63 percent and 48 percent respectively.

An index developed by Aberdeen to measure the maturity of an organization’s compliance efforts shows that although the healthcare organizations surveyed were mature in their data management efforts, they were far less developed in their compliance efforts when they stored and protected data, syndicated data between two applications, ingested data into a central repository or integrated data from multiple, disparate sources.

The immaturity of compliance efforts has real-world consequences for healthcare entities. Four out of five (81 percent) study participants reported at least one data privacy and non-compliance issue in the past year, and two out of three (66 percent) reported at least one data breach in the past year.

It isn’t surprising to find that healthcare organizations struggle with data security. The complexity and number of types of data and data-related processes in healthcare is daunting. In addition to PHI, hospitals and their affiliates handle financial transactions, personally identifiable information, employee records, and confidential or intellectual property records. Adding to the challenge of protecting this information is the ever-increasing use of mobile devices in clinical and business areas of the healthcare organization.

In addition to the complexities of data management and integration, there are budgetary considerations. As healthcare organizations face increasing financial challenges, investment in new technology and the IT personnel to manage it can be formidable. However, healthcare participants in the Aberdeen study reported a median of 37 percent of the overall IT budget dedicated to investment in compliance activities. Study participants from life sciences and other industries included in Aberdeen’s total study reported lower budget commitments to compliance.

This raises the question: If healthcare organizations are investing in compliance activities, why do we still see significant data breaches, fines for non-compliance and difficulty reaching full compliance?

While there are practical steps that every privacy and security officer should take to ensure the organization is compliant with HIPAA, there are also technology options that enhance a healthcare entity’s ability to better manage data integration from multiple sources and address compliance requirements.

An upcoming webinar, The State of Privacy and Security Compliance for Enterprise Data: “Why Are We Doing This Ourselves?” discusses the Aberdeen survey results and presents advice on how healthcare IT leaders can evaluate their compliance-readiness and identify potential solutions can provide some thought-provoking guidance.

One of the solutions is the use of third-party providers who can provide the data integration and management needs of the healthcare organization to ensure compliance with data security requirements. This strategy can also address a myriad of challenges faced by hospitals. Not only can the expertise and specialty knowledge of the third-party take a burden off in-house IT staff but choosing a managed services strategy that eliminates the need for a significant upfront investment enables moving the expense from the IT capital budget to the operating budget with predictable recurring costs.

Freeing capital dollars to invest in other digital transformation strategies and enabling IT staff to focus on mission-critical activities in the healthcare organization are benefits of exploring outsource opportunities with the right partner.

More importantly, moving toward a higher level of compliance with data security requirements will improve the likelihood of a good night’s sleep!

About Lance Pilkington
Lance Pilkington is the Vice President of Global Compliance at Liaison Technologies, a position he has held since joining the company in September 2012. Lance is responsible for establishing and leading strategic initiatives under Liaison’s Trust program to ensure the company is consistently delivering on its compliance commitments. Liaison Technologies is a proud sponsor of Healthcare Scene.

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.

Revenue Cycle Trends To Watch This Year

Posted on July 13, 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.

Revenue cycle management is something of a moving target. Every time you think you’ve got your processes and workflow in line, something changes and you have to tweak them again. No better example of that was the proposed changes to E/M that came out yesterday. While we wait for that to play out, here’s one look at the trends influencing RCM strategies this year, according to Healthcare IT leaders revenue cycle lead Larry Todd, CPA.

Mergers

As healthcare organizations merge, many legacy systems begin to sunset. That drives them to roll out new systems that can support organizational growth. Health leaders need to figure out how to retire old systems and embrace new ones during a revenue cycle implementation. “Without proper integrations, many organizations will be challenged to manage their reimbursement processes,” Todd says.

Claims denial challenges

Providers are having a hard time addressing claims denials and documentation to support appeals. RCM leaders need to find ways to tighten up these processes and reduce denial rates. They can do so either by adopting third-party systems or working within their own infrastructure, he notes.

CFO engagement

Any technology implementation will have an impact on revenue, so CFOs should stay engaged in the rollout process, he says. “These are highly technical projects, so there’s a tendency to hand over the reins to IT or the software vendor,” notes Todd, a former CFO. “But financial executives need to stay engaged throughout the project, including weekly implementation status updates.”

Providers should form a revenue cycle action team which includes all the stakeholders to the table, including the CFO and clinicians, he says. If the CFO is involved in this process, he or she can offer critical executive oversight of decisions made that impact A/R and cash.

User training and adoption

During the transition from a legacy system to a new platform, healthcare leaders need to make sure their staff are trained to use it. If they aren’t comfortable with the new system, it can mean trouble. Bear in mind that some employees may have used the legacy system for many years and need support as they make the transition. Otherwise, they may balk and productivity could fall.

Outside expertise

Given the complexity of rolling out new systems, it can help to hire experts who understand the technical and operational aspects of the software, along with organizational processes involved in the transition. “It’s very valuable to work with a consulting firm that employs real consultants – people who have worked in operations for years,” Todd concludes.

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.

Geisinger Integrates Precision Medicine Into Care

Posted on May 21, 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.

Lately, it seems like we read about new advances in precision medicine every day. Increasingly, physicians are able to adjust drug therapies and predict conditions like cancer and heart disease before they blossom, particularly in the case of some cancers. However, many health organizations are still focused on research rather than delivering genomic medicine results to consumers.

The process of basing medical decisions on genomic data has certainly begun, with a number of health systems jumping on board. For example, a few months ago Intermountain Healthcare begin the process of validating and launching several tests designed to identify hereditary genetic patterns that might lead to disease. Intermountain expects this work to be particularly fruitful for individuals with a family history of breast cancer or ovarian cancer. The test should identify both those previously diagnosed with cancer and healthy individuals with hereditary cancer gene mutations.

Now, at least one health system is taking things even further. Geisinger Health says it has announced that it plans to expand its genomics program beyond its research phase and into everyday care for all patients. The new program will not only target patients who have obvious symptoms, but instead, all patients Geisinger treats. The health systems clinical DNA sequencing efforts will begin with a 1000-patient pilot program taking place in mid-to-late 2018.

According to David Ledbetter, Ph.D., Geisinger executive vice president and chief scientific officer, the program will not only help current patients but also amass data that will help future patients. “As we sequence the exomes of our patients and learn even more about particular genome variants and their impact on different health conditions, we predict that as many as 10 to 15 percent of our patients will benefit,” he said.

The new strategy follows on the success of its MyCode Community Health Initiative, which it launched in 2014 in collaboration with Regeneron Pharmaceuticals. Since then, Geisinger has been analyzing the DNA of patients participating in the program, which has attracted more than 190,000 patient sign-ups to date. To date, more than 500 MyCode participants have been notified that they have a genomic variant which increases the chance that they’ll develop cancer or heart disease.

Geisinger’s effort sounds exciting, there’s little doubt. However, programs like these face some obstacles which the health system wouldn’t call attention to a press release. For example, as my colleague John Lynn notes, integrating genomic data with other clinical information could be quite difficult, and sharing it even more so.

“Healthcare organizations have problems even sharing something as standard and simple as a PDF,” he wrote last year. “Once we have real genomic data and the markers behind them, EHRs won’t have any idea how to handle them. We’ll need a whole new model and approach or our current interoperability problems will look like child’s play.” Let’s hope the industry develops this new approach soon.

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.

Improving Data Outcomes: Just What The Doctor Ordered

Posted on May 8, 2018 I Written By

The following is a guest blog post by Dave Corbin, CEO of HULFT.

Health care has a data problem. Vast quantities are generated but inefficiencies around sharing, retrieval, and integration have acute repercussions in an environment of squeezed budgets and growing patient demands.

The sensitive nature of much of the data being processed is a core issue. Confidential patient information has traditionally encouraged a ‘closed door’ approach to data management and an unease over hyper-accessibility to this information.

Compounding the challenge is the sheer scale and scope of the typical health care environment and myriad of departmental layers. The mix of new and legacy IT systems used for everything from billing records to patient tracking often means deep silos and poor data connections, the accumulative effect of which undermines decision-making. As delays become commonplace, this ongoing battle to coordinate disparate information manifests itself in many different ways in a busy hospital.

Optimizing bed occupancies – a data issue?

One example involves managing bed occupancy, a complex task which needs multiple players to be in the loop when it comes to the latest on a patient’s admission or discharge status. Anecdotal evidence points to a process often informed manually via feedback with competing information. Nurses at the end of their shift may report that a patient is about to be discharged, unaware that a doctor has since requested more tests to be carried out for that patient. As everyone is left waiting for the results from the laboratory, the planned changeover of beds is delayed with many knock-on effects, increasing congestion and costs and frustrating staff and patients in equal measure.

How data is managed becomes a critical factor in tackling the variations that creep into critical processes and resource utilization. In the example above, harnessing predictive modelling and data mining to forecast the number of patient discharges so that the number of beds available for the coming weeks can be estimated more accurately will no doubt become an increasingly mainstream option for the sector.

Predictive analytics is great and all, but first….

Before any of this can happen, health care organizations need a solid foundation of accessible and visible data which is centralized, intuitive, and easy to manage.

Providing a holistic approach to data transfer and integration, data logistics can help deliver security, compliance, and seamless connectivity speeding up the processing of large volumes of sensitive material such as electronic health records – the kind of data that simply cannot be lost. These can ensure the reliable and secure exchange of intelligence with outside health care vendors and partners.

For data outcomes, we’re calling for a new breed of data logistics that’s intuitive and easy to use. Monitoring interfaces which enable anyone with permission to access the network to see what integrations and transfers are running in real time with no requirement for programming or coding are the kind of intervention which opens the data management to a far wider section of an organization.

Collecting data across a network of multiple transfer and integration activities and putting it in a place where people can use, manage and manipulate becomes central to breaking down the barriers that have long compromised efficiencies in the health care sector.

HULFT works with health care organizations of all sizes to establish a strong back-end data infrastructure that make front-end advances possible. Learn how one medical technology pioneer used HULFT to drive operational efficiencies and improve quality assurance in this case study.

Dave Corbin is CEO of HULFT, a comprehensive data logistics platform that allows IT to find, secure, transform and move information at scale. HULFT is a proud sponsor of Health IT Expo, a practical innovation conference organized by Healthcare Scene.  Find out more at hulftinc.com

Health Leaders Say Automating Patient Engagement Efforts Will Have Major Impact

Posted on March 12, 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.

Over the last few years, many vendors have rolled out products designed to engage patients further in their care. According to a new study, these solutions may be just the tip of the iceberg. In fact, many healthcare executives see patient-facing, engagement-enhancing technology as critical to the future of healthcare, according to a new study.

The study, by the World Business Group, focuses on technology that can link patients with care in between visits to their primary care center. Patient engagement technologies, which they call “automated care,” have the potential to bridge such gaps by providing AI-based assistance to consumers.

The survey, which was also backed by Conversa Health, drew on direct interviews and survey responses from 134 health execs. The researchers looked at how those execs viewed automated healthcare technologies, how these technologies might be used and whether respondents plan to adopt them.

Respondents were clearly very enthusiastic about these tools. Nearly all (98%) said they believed automated healthcare will be important in creating a continuous, collaborative relationship with providers. The survey also found that 87% of respondents felt that automated healthcare will be helpful in getting patients to engage with their own care.

The next step, of course, is throwing resources at the problem — and it’s happening. Seventy-nine percent of survey respondents said they expected to work on integrating automated healthcare in their organization within the next two years. Also, 44% said that they had a chief patient experience officer or other executive with an equivalent title on board within their organization. This development is fairly new, however, as 40% of these organizations said that the role has existed for two years or less.

Meanwhile, several respondents felt that automating patient healthcare could generate a positive feedback loop. Forty-nine percent of those surveyed reported that they were either integrating or have already integrated patient-generated health data, which they expect, in turn, to integrate into the patient experience efforts.

Among organizations working with patient-generated health data, 73% were gathering patient health histories, 64% treatment histories, 59% lifestyle or social data, 52% symptoms data, and 32% biometric data.

Thirty percent said they were beginning to integrate such data and collect it work effectively, 28% said they could collect some PGHD but had trouble integrating with their systems, 14% said they were just beginning to collect such data and 9% said they were not able to collect this data at all. Just 19% reported they were fully able to collect integrate PGHD and use it to improve patient experiences.

All told, it appears we’re on the cusp of a major change in the role patient services play in provider outreach. It will probably be a few more years before we have a good idea of where all this is headed, but my guess is that it’s heading somewhere useful.

Yale New Haven Hospital Partners With Epic On Centralized Operations Center

Posted on February 5, 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.

Info, info, all around, and not a place to manage it all. That’s the dilemma faced by most hospitals as they work to leverage the massive data stores they’re accumulating in their health IT systems.

Yale New Haven Hospital’s solution to the problem is to create a centralized operations center which connects the right people to real-time data analytics. Its Capacity Command Center (nifty alliteration, folks!) was created by YNHH, Epic and the YNHH Clinical Redesign Initiative.

The Command Center project comes five years into YNHH’s long-term High Reliability project, which is designed to prepare the institution for future challenges. These efforts are focused not only on care quality and patient safety but also managing what YNHH says are the highest patient volumes in Connecticut. Its statement also notes that with transfers from other hospitals increasing, the hospital is seeing a growth in patient acuity, which is obviously another challenge it must address.

The Capacity Command Center’s functions are fairly straightforward, though they have to have been a beast to develop.

On the one hand, the Center offers technology which sorts through the flood of operational data generated by and stored in its Epic system, generating dashboards which change in real time and drive process changes. These dashboards present real-time metrics such as bed capacity, delays for procedures and tests and ambulatory utilization, which are made available on Center screens as well as within Epic.

In addition, YNHH has brought representatives from all of the relevant operational areas into a single physical location, including bed management, the Emergency Department, nursing staffing, environmental services and patient transport. Not only is this a good approach overall, it’s particularly helpful when patient admissions levels climb precipitously, the hospital notes.

This model is already having a positive impact on the care process, according to YNHH’s statement. For example, it notes, infection prevention staffers can now identify all patients with Foley catheters and review their charts. With this knowledge in hand, these staffers can discuss whether the patient is ready to have the catheter removed and avoid related urinary tract infections associated with prolonged use.

I don’t know about you, but I was excited to read about this initiative. It sounds like YNHH is doing exactly what it should do to get more out of patient data. For example, I was glad to read that the dashboard offered real-time analytics options rather than one-off projections from old data. Bringing key operational players together in one place makes great sense as well.

Of course, not all hospitals will have the resources to pull something off something like this. YNHH is a 1,541-bed giant which had the cash to take on a command center project. Few community hospitals would have the staff or money to make such a thing happen. Still, it’s good to see somebody at the cutting edge.

The 4 P’s of Innovation in Health Science

Posted on January 31, 2018 I Written By

Sunny is a serial entrepreneur on a mission to improve quality of care through data science. Sunny’s last venture docBeat, a healthcare care coordination platform, was successfully acquired by Vocera communications. Sunny has an impressive track record of Strategy, Business Development, Innovation and Execution in the Healthcare, Casino Entertainment, Retail and Gaming verticals. Sunny is the Co-Chair for the Las Vegas Chapter of Akshaya Patra foundation (www.foodforeducation.org) since 2010.

You’ll never meet anyone that loves health data science more than Prashant Natarajan. He literally wrote the book on the subject (Check out Demystifying Big Data and Machine Learning for Healthcare to see why I mean literally). He recently gave a presentation on the 4 P’s of Innovation in Health Science which included this slide:

Sadly, I couldn’t find a recording of his presentation. However, this slide puts health data science in perspective. Prashant boiled it down to 4 simple points. The problem is that too many healthcare organizations are unable to really execute all 4 P’s in their health science innovation efforts.

No doubt each of these 4 P’s is challenging, but the most challenging one I see today is the first P: People.

I’m not sure all of the ways that Prashant addresses the people problem, but it’s somewhat ironic that people is the biggest problem with health science innovation. I see the challenge as two fold. First, finding people who have the health science mindset are hard to find. Competition for people with these skills is fierce and many of them don’t want to get into healthcare which is complex, regulated, and often behind.

The second major health science challenge revolves around the people who collect, aggregate, and enter the data. It’s easy for a front line person to not care about the downstream effects of them entering poor quality data. Not to mention being consistent in what you enter and how you enter it.

It’s somewhat apart of human nature for us to jimmy rig a solution to the problem we face. Those workaround solutions wreaked havoc downstream in your data science efforts. I recently heard the example of a hospital always choosing Mongolian for some setting because it was a setting that would never be used otherwise. The culture of the hospital just knew this is what to do. Once the data scientists started looking at the data they wondered why this Mongolian population kept coming up in their results. Every healthcare organization has their “mongolian” workaround that causes havoc on data science.

What do you think of these 4 Ps of Innovation in Health Science? Is there something missing? Do you see one of these as more important than another?