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New Service Brings RCM Process To Blockchain

Posted on October 6, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Much of the discussion around blockchain (that I’ve seen, at least) focuses on blockchain’s potential as a platform for secure sharing of clinical data. For example, some HIT experts see blockchain as a near-ideal scalable platform for protecting the privacy of EHR-based patient data.

That being said, blockchain offers an even more logical platform for financial transactions, given its origins as the foundation for bitcoin transactions and its track record of supporting those transactions efficiently.

Apparently, that hasn’t been lost on the team at Change Healthcare. The Nashville-based health IT company is planning to launch what it says is the first blockchain solution for enterprise-scale use in healthcare. According to a release announcing the launch, the new technology platform should be online by the end of this year.

Change Healthcare already processes 12 billion transactions a year, worth more than $2 trillion in claims annually.  Not surprisingly, the new platform will extend its new blockchain platform to its existing payer and provider partners. Here’s an infographic explaining how Change expects processes will shift when it deploys blockchain:

Change_Healthcare_Intelligent_Healthcare_Network_Workflow_Infographic

To build out blockchain for use in RCM, Change is working with customers, as well as organizations like The Linux Foundation’s Hyperledger project.

Hyperledger encompasses a range of tools set to offer new, more-standardized approaches to deploying blockchain, including Hyperledger Cello, which will offer access to on-demand “as-a-service” blockchain technology and Hyperledger Composer, a tool for building blockchain business networks and boosting the development and deployment of smart contracts.

It’s hard to tell how much impact Change’s blockchain deployment will have. Certainly, there are countless ways in which RCM can be improved, given the extent to which dollars still leak out of the system. Also, given its existing RCM network, Change has as good a chance as anyone of building out blockchain-based RCM.

Still, I’m wondering whether the new service will prove to be a long-term product deployment or an experiment (though Change would doubtless argue for the former). Not only that, given its relatively immature status and the lack of broadly-accepted standards, is it really safe for providers to rely on blockchain for something as mission-critical as cash flow?

Of course, when it comes to new technologies, somebody has to be first, and I’m certainly not suggesting that Change doesn’t know what it’s doing. I’d just like more evidence that blockchain is ready for prime time.

FDA Announces Precertification Program For Digital Health Tools

Posted on October 5, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

The FDA has recruited some the world’s top technology and medical companies to help it pilot test a program under which digital health software could be marketed without going through the through the agency’s entire certification process.

The participants, which include Apple, Fitbit, Johnson & Johnson, Samsung and Roche, will give the agency access to the measures they’re using to develop, test and maintain their software, and also how they collect post-market data.

Once armed with this information, the FDA will leverage it to determine the key metrics and performance indicators it uses to see if digital health software meets its quality standards.

Companies that meet these new standards could become pre-certified, a status which grants them a far easier path to certification than in the past. This represents a broad shift in the FDA’s regulatory philosophy, “looking first at the software developer digital health technology developer, not the product,” according to a report previously released by the agency.

If the pilot works as planned, the FDA is considering making some significant changes to the certification process. If their processes pass muster, pre-certified companies may be allowed to submit less information to the FDA than they currently must before marketing a new digital health tool.  The agency is also considering the more radical step of allowing pre-certified companies to avoid submitting a product for premarket review in some cases. (It’s worth noting that these rules would apply to lower-risk settings.)

The prospect of pre-certifying companies does raise some concerns. In truth, the argument could be made that digital health software should be regulated more tightly, not less. In particular, the mobile healthcare world is still something of a lawless frontier, with very few apps facing privacy, security or accuracy oversight.

The fact is, it’s little wonder that physicians aren’t comfortable using mobile health app data given how loosely it can be constructed at times, not to mention the reality that it might not even measure basic vital signs reliably.

It’s not that the healthcare industry isn’t aware of these issues. about a year ago, a group of healthcare organizations including HIMSS, the American Medical Association and the American Heart Association came together to develop a framework of principles dressing app quality. Still, that’s far short of establishing a certification body.

On the other hand, the FDA does have a point when it notes that a pre-certification program could make it easier for useful digital health tools to reach the marketplace. Assuming the program is constructed well, it seems to me that this is a good idea.

True, it’s pretty unusual to see the FDA loosen up its certification process – a fairly progressive move for a stodgy agency – while the industry fails to self-regulate, but it’s a welcome change of style. I guess digital health really is changing things up.

 

Eliminate These Five Flaws to Improve Asset Utilization in Healthcare

Posted on October 4, 2017 I Written By

The following is a guest blog post by Mohan Giridharadas, Founder and CEO, LeanTaaS.

The passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act accelerated the deployment of electronic health records (EHRs) across healthcare. The overwhelming focus was to capture every patient encounter and place it into an integrated system of records. Equipped with this massive database of patient data, health systems believed they could make exponential improvements to patient experiences and outcomes.

The pace of this migration resulted in some shortcuts being taken — the consequences of which are now apparent to discerning CFOs and senior leaders. Among these shortcuts was the use of resources and capacity as the basis of scheduling patients; this concept is used by hundreds of schedulers in every health system. While simple to grasp, the definition is mathematically flawed.

Not being able to offer a new patient an appointment for at least 10 days negatively impacts the patient experience. Likewise, exceeding capacity by scheduling too many appointments results in long wait times for patients, which also negatively impacts their experience. The troubling paradox is that the very asset creating long wait times and long lead times for appointments also happens to perform at ~50 percent utilization virtually every day. The impact of a mathematically flawed foundation results in alternating between overutilization (causing long patient wait times and/or long delays in securing an appointment) and under-utilization (a waste of expensive capital and human assets).

Here are five specific flaws in the mathematical foundation of health system scheduling:

1. A medical appointment is a stochastic — not deterministic — event.

Every health system has some version of this grid — assets across the top, times of the day for each day of the week along the side — on paper, in electronic format or on a whiteboard. The assets could be specific (e.g., the GE MRI machine or virtual MRI #1, #2, etc.). As an appointment gets confirmed, the appropriate range of time on the grid gets filled in to indicate that the slot has been reserved.

Your local racquet club uses this approach to reserve tennis courts for its members. It works beautifully because the length of a court reservation is precisely known (i.e., deterministic) to be exactly one hour in duration. Imagine the chaos if club rules were changed to allow players to hold their reservation even if they arrive late (up to 30 minutes late) and play until they were tired (up to a maximum of two hours). This would make the start and end times for a specific tennis appointment random (i.e., stochastic). Having a reservation would no longer mean you would actually get on the court at your scheduled time. This happens to patients every day across many parts of a health system. The only way to address the fact that a deterministic framework was used to schedule a stochastic event is to “reserve capacity” either in the form of a time buffer (i.e., pretend that each appointment is actually longer than necessary) or as an asset buffer (i.e., hold some assets in reserve).

2. The asset cannot be scheduled in isolation; a staff member has to complete the treatment.

Every appointment needs a nurse, provider or technician to complete the treatment. These staff members are scheduled independently and have highly variable workloads throughout the day. Having an asset that is available without estimating the probability of the appropriate staff member also being available at that exact time will invariably result in delays. Imagine if the tennis court required the club pro be present for the first 10 and last 10 minutes of every tennis appointment. The grid system wouldn’t work in that case either (unless the club was willing to have one tennis pro on the staff for every tennis court).

3. It requires an estimation of probabilities.

Medical appointments have a degree of randomness — no-shows, cancellations and last-minute add-ons are a fact of life, and some appointments run longer or shorter than expected. Every other scheduling system faced with such uncertainty incorporates the mathematics of probability theory. For example, airlines routinely overbook their flights; the exact number of overbooked seats sold depends on the route, the day and the flight. They usually get it right, and the cancellations and no-shows create enough room for the standby passengers. Occasionally, they get it wrong and more passengers hold tickets than the number of seats on the airplane. This results in the familiar process of finding volunteers willing to take a later flight in exchange for some sort of compensation. Nothing in the EHR or scheduling systems used by hospitals allows for this strategic use of probability theory to improve asset utilization.

4. Start time and duration are independent variables.

Continuing with the airplane analogy: As a line of planes work their way toward the runway for departure, the controller really doesn’t care about each flight’s duration. Her job is to get each plane safely off the ground with an appropriate gap between successive takeoffs. If one 8-hour flight were to be cancelled, the controller cannot suddenly decide to squeeze in eight 1-hour flights in its place. Yet, EHRs and scheduling systems have conflated start time and appointment duration into a single variable. Managers, department leaders and schedulers have been taught that if they discover a 4-hour opening in the “appointment grid” for any specific asset, they are free to schedule any of the following combinations:

  • One 4-hour appointment
  • Two 2-hour appointments
  • One 2-hour appointment and two 1-hour appointments in any order
  • One 3-hour appointment and one 1-hour appointment in either order
  • Four 1-hour appointments

These are absolutely not equivalent choices. Each has wildly different resource-loading implications for the staff, and each choice has a different probability profile of starting or ending on time. This explains why the perfectly laid out appointment grid at the start of each day almost never materializes as planned.

5. Setting appointments is more complicated than first-come, first-served.

Schedulers typically make appointments on a first-come, first-served basis. If a patient were scheduling an infusion treatment or MRI far in advance, the patient would likely hear “the calendar is pretty open on that day — what time would you like?” What seems like a patient-friendly gesture is actually mathematically incorrect. The appointment options for each future day should be a carefully orchestrated set of slots of varying durations that will result in the flattest load profile possible. In fact, blindly honoring patient appointment requests just “kicks the can down the road”; the scheduler has merely swapped the inconvenience of appointment time negotiation for excessive patient delays on the day of treatment. Instead, the scheduler should steer the patient to one of the recommended appointment slots based on the duration for that patient’s specific treatment.

In the mid-1980s, Sun Microsystems famously proclaimed that the “network is the computer.” The internet and cloud computing were not yet a thing, so most people could not grasp the concept of computers needing to be interconnected and that the computation would take place in the network and not on the workstation. In healthcare scheduling, “the duration is the resource” — the number of slots of a specific duration must be counted and allocated judiciously at various points throughout the day. Providers should carefully forecast the volume and the duration mix of patients they expect to serve for every asset on every day of the week. With that knowledge the provider will know, for example, that on Mondays, we need 10 1-hour treatments, 15 2-hour treatments and so on. Schedulers could then strategically decide to space appointments throughout the day (or cluster them in the morning or afternoon) by offering up two 1-hour slots at 7:10 a.m., one 1-hour slot at 7:40 a.m., etc. The allocation pattern matches the availability of the staff and the underlying asset to deliver the most level-loaded schedule for each day. In this construct, the duration is the resource being offered up to patients one at a time with the staff and asset availability as mathematical constraints to the equation (along with dozens of other operational constraints).

Health systems need to re-evaluate the mathematical foundation used to guide their day-to-day operations — and upon which the quality of the patient experience relies. All the macro forces in healthcare (more patients, older patients, higher incidence of chronic illnesses, lower reimbursements, push toward value-based care, tighter operating and capital budgets) indicate an urgent need to be able to do more with existing assets without upsetting patient flow. A strong mathematical foundation will enable a level of operational excellence to help health systems increase their effective capacity for treating more patients while simultaneously improving the overall flow and reducing the wait time.

About Mohan Giridharadas
Mohan Giridharadas is an accomplished expert in lean methodologies. During his 18-year career at McKinsey & Company (where he was a senior partner/director for six years), he co-created the lean service operations practice and ran the North American lean manufacturing and service operations practices and the Asia-Pacific operations practice. He has helped numerous Fortune 500 companies drive operational efficiency with lean practices. As founder and CEO of LeanTaaS, a Silicon Valley-based innovator of cloud-based solutions to healthcare’s biggest challenges, Mohan works closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, NewYork-Presbyterian, Cleveland Clinic, MD Anderson and more. Mohan holds a B.Tech from IIT Bombay, MS in Computer Science from Georgia Institute of Technology and an MBA from Stanford GSB. He is on the faculty of Continuing Education at Stanford University and UC Berkeley Haas School of Business and has been named by Becker’s Hospital Review as one of the top entrepreneurs innovating in healthcare. For more information on LeanTaaS, please visit http://www.leantaas.com and follow the company on Twitter @LeanTaaS, Facebook at https://www.facebook.com/LeanTaaS and LinkedIn at https://www.linkedin.com/company/leantaas.

After Death Data Donation – A #hITsm Halloween Horror Chat

Posted on October 3, 2017 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.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 10/6 at Noon ET (9 AM PT). This week’s chat will be hosted by Regina Holliday (@ReginaHolliday), Founder of #TheWalkingGallery on the topic of “After Death Data Donation.”

Since this month is October (which is heavily associated with death and horror in western cultures) and this week is National HIT week, I thought we would combine the two and talk about death and data donation. Since the 1970’s the autopsy rate in the US has plummeted to less than 10%. When the results of the autopsies are evaluated, in 30% cases the cause of death on the death certificate is a misdiagnosis.

In EHR data collection, the system is designed to capture data of a live patient and data collection stops once a patient dies. Let’s explore these topics in this week’s #hITsm Twitter chat.

References:

Here are the questions that will serve as the framework for this week’s #HITsm chat:
T1: How can we create a system that provides more access to autopsies? #HITSM

T2: How do we collect autopsy data through the EHR for quality control and public health? #HITsm

T3: How do we change a status quo that is willing to look the other way when faced with the reality of poor data about death? #HITsm

T4: How can we make after death data donation a reality for patient families? #HITsm

T5: Some states still have their autopsy data in paper systems. Does ONC need a meaningful use for a meaningful death? #HITsm

Bonus: The CDC did a great job reminding folks about disaster preparedness with their Zombie campaign. Can the do something like that to highlight the need for cause of death data? #HITsm

Upcoming #HITsm Chat Schedule
10/13 – Role of Provider Engagement for Improving Data Accuracy
Hosted by @CAQH

10/20 – Community Sharing Chat
Hosted by the #HITsm Community

10/27 – Aggregating the Patient Perspective and Incorporating It Into Software to Change Healthcare
Hosted by Lisa Davis Budzinski (@lisadbudzinski)

We look forward to learning from the #HITsm community! As always, let us know if you’d like to host a future #HITsm chat or if you know someone you think we should invite to host.

If you’re searching for the latest #HITsm chat, you can always find the latest #HITsm chat and schedule of chats here.

Top Five Challenges of Healthcare Cloud Deployments and How to Solve Them

Posted on October 2, 2017 I Written By

The following is a guest blog post by Chad Kissinger, Founder of OnRamp.

According to the HIMSS 2016 Survey, 84 percent of providers are currently using a cloud service, showing security and compliance issues are not preventing organizations from deploying cloud environments. Despite growing adoption rates, breaches and security incidents continue to rise. Cloud deployments and ongoing environment management errors are to blame. 

Cloud services offer clear benefits—performance, cost savings, and scalability to name a few—so it’s no wonder healthcare organizations, like yours, are eager to take advantage of all that the cloud has to offer. Unfortunately, vulnerabilities are often introduced to your network when you adopt new technology. Let’s discuss how to identify and overcome common challenges in secure, compliant cloud deployments so you can opportunistically adopt cloud-based solutions while remaining on the right side of the law.

1. Ambiguous Delegation of Responsibilities
When technology is new to an organization, the responsibility of finding and managing that solution is often unclear. You must determine who owns your data. Is it your IT Department? Or perhaps your Security Department? It’s difficult to coordinate different people across departments, and even more difficult to communicate effectively between your organization and your provider. The delegation of responsibilities between you and your business associate will vary based on your service model—i.e. software as a service, infrastructure as a service, etc.

To prevent these issues, audit operational and business processes to determine the people, roles, and responsibilities for your team internally. Repeat the process for those services you will outsource to your cloud provider. Your business associate agreement should note the details of each party’s responsibilities, avoiding ambiguity and gaps in security or compliance. Look for provider credentials verified by third-party entities that demonstrate security levels at the data center level, such as HITRUST CSF and SSAE 16 SOC 2 Type 2 and SOC3.

2.    Lack of Policies, Standards, and Security Practices
If your organization doesn’t have a solid foundation of policies, standards, and security practices, you will likely experience one or more of the security-related issues outlined below. It’s necessary to not only create policies, but also ensure your organization is able to enforce them consistently.

  • Shadow IT. According to a recent HyTrust Cloud Survey of 51 organizations, 40% of cloud services are commissioned without IT input.
  • Cloud Portability and Mobility. Mitigating risks among many endpoints, from wearables to smart beds, becomes more difficult as you add more end points.
  • Privileged User Access. Divide your user access by work role and limit access to mitigate malicious insider attacks.
  • Ongoing Staff Education and Training. Your team needs to be properly trained in best practices and understand the role that they play in cybersecurity.

Proper security and compliance also involves the processes that safeguard your data and the documentation that proves your efforts. Such processes include auditing operational and business processes, managing people, roles and identities, ensuring proper protection of data and information, assessing the security provisions for cloud applications, and data decommissioning.

Communicate your security and compliance policies to your cloud provider to ensure their end of the operations falls in line with your overall plan.

3. Protecting Data and Meeting HIPAA Controls
The HIPAA Privacy Rule, the HIPAA Security Rule, and HITECH all aim to secure your electronic protected health information (ePHI) and establish the national standards. Your concern is maintaining the confidentiality, availability, and integrity of sensitive data. In practice, this includes:

  • Technology
  • Safeguards (Physical & Administrative)
  • Process
  • People
  • Business Associates & Support
  • Auditable Compliance

Network solution experts recognize HIPAA compliant data must be secure, but also needs to be readily available to users and retain integrity across platforms. Using experienced cloud solution providers will bridge the gap between HIPAA requirements, patient administration, and the benefit of technology to treat healthcare clients and facilitate care.

Seek the right technology and implement controls that are both “required and addressed” within HIPAA’s regulations. When it comes to security, you can never be too prepared. Here are some of the measures you’ll want to implement:

  • Data encryption in transit and at rest
  • Firewalls
  • Multi-factor Authentication
  • Cloud Encryption Key Management
  • Audit logs showing access to ePHI
  • Vulnerability scanning, intrusion detection/prevention
  • Hardware and OS patching
  • Security Audits
  • Contingency Planning—regular data backup and disaster recovery plan

The number one mistake organizations make in protected data in a cloud deployment is insufficient encryption, followed by key management. Encryption must be FIPS 140-2 compliant.

4.    Ensuring Data Availability, Reliability, and Integrity
The key to service reliability and uptime is in your data backups and disaster recovery (DR) efforts. Data backup is not the same as disaster recovery—this is a common misconception. Data backup is part of business continuity planning, but requires much more. There’s a gap between how organizations perceive their track records and the reality of their DR capabilities. The “CloudEndure Survey of 2016” notes that 90% of respondents claim they meet their availability, but only 38% meet their goals consistently, and 22% of the organizations surveyed don’t measure service availability at all. Keep in mind that downtime can result from your cloud provider—and this is out of your control. For instance, the AWS outage earlier this year caused a ruckus after many cloud-based programs stopped functioning.

5.    Ability to Convey Auditable Compliance (Transparency)
Investors, customers, and regulators cannot easily discern that your cloud environment is compliant because it’s not as visible as other solutions, like on-premise hosting. You will have to work closely with your cloud provider to identify how to document your technology, policies, and procedures in order to document your efforts and prove auditable compliance.

Putting It All Together
The cloud provides significant advantages, but transitioning into the cloud requires a thorough roadmap with checkpoints for security and compliance along the way. Remember that technology is just the first step in a secure cloud deployment—proper security and compliance also involves the processes that protect your sensitive data and the documentation that proves your compliance efforts. You’ll want to identify resources from IT, security and operations to participate in your cloud deployment process, and choose a cloud provider that’s certified and knowledgeable in the nuances of healthcare cloud deployments.

For more information download the white paper “HOW TO DEPLOY A SECURE, COMPLIANT CLOUD FOR HEALTHCARE.”

About OnRamp

OnRamp is a HITRUST-certified data center services company that specializes in high security and compliant hybrid hosting and is a proud sponsor of Healthcare Scene. Our solutions help organizations meet compliance standards including, HIPAA, PCI, SOX, FISMA and FERPA. As an SSAE 16 SOC 2 Type 2 and SOC 3, PCI-DSS certified, and HIPAA compliant company, OnRamp operates multiple enterprise-class data centers to deploy cloud computing, colocation, and managed services. Visit www.onr.com or call 888.667.2660 to learn more.

Why Should Patients Control Their Health Data? Here Are A Few Ideas.

Posted on September 29, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Lately, healthcare organizations have begun working to give patients more access to their personal health data. They’ve concluded that the more control patients have, the more engaged they become in your care, which in turn leads to better outcomes.

But patient engagement isn’t the only reason for giving patients the keys to their PHI. In fact, organizational control of patient health data can cause problems for everyone in the healthcare data exchange chain.

An item found on the Allscripts blog does a nice job of articulating issues that can arise.  According to the blog item, those issues include the following:

  • The patient is in the best position to address inconsistencies in their medical record. For example, if one doctor diagnoses the patient with asthma, then another physician conclusively demonstrates the patient is not asthmatic, the patient can reconcile the two physicians’ conclusions.
  • Patients have a better overview of their care than most doctors. When a chronically ill patient sees multiple clinicians, their impressions may conflict with one another, but the patient can provide context on their overall conditions.
  • If a patient consents to multiple uses of their health data, and the consents seem to be in conflict, only the patient can articulate what their intentions were.
  • If the master patient indexing process generates a false match with someone else’s records, the patient will recognize this immediately, while physicians may not.
  • Giving patients control of the record allows them to decide how long those records should be maintained. Otherwise, HIEs — or other entities not bound by record retention laws — might destroy the data prematurely.
  • When patients have control of their data, they can make sure it gets to whomever they choose. On the other hand, patient data may not make it to other care settings if providers drop the ball.

To be sure, delegating control of their PHI to patients can go too far.

For example, if they’re transmitting most or all of their health data between providers, it could pose a significant administrative burden.  Patients may not have the time or energy to route the data files between their providers, assure that data has been received on the other end and make certain that the data was formatted in a way their clinicians can use.

Also, if the patient is chronically ill and sees multiple providers, they may end up having to manage a large body of data files, and not everyone can do so effectively. Ultimately, they may get too overwhelmed to send their records to anyone, or send the wrong records, which can create complications of its own.

Still, on the whole, healthcare organizations are giving patients more control of their health data for good reasons. When patients take responsibility for their health data, they’re far more likely to understand their condition and take steps to address problems. Establishing a balance between patient and provider control may be tricky, but it can and should be done.

NY-Based HIE Captures One Million Patient Consents

Posted on September 28, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

One of the big obstacles to the free exchange of health data is obtaining patient consent to share that data. It’s all well and good if we can bring exchange partners onto a single data sharing format, but if patients don’t consent to that exchange things get ugly. It’s critical that healthcare organizations solve this problem, because without patient consent HIEs are dead in the water.

Given these issues, I was intrigued to read a press release from HEALTHeLINK, an HIE serving Western New York, which announced that it had obtained one million patient consents to share their PHI. HEALTHeLINK connects nearly 4,600 physicians, along with hospitals, health plans and other healthcare providers. It’s part of a larger HIE, the Statewide Health Information Network of New York.

How did HEALTHeLINK obtain the consents? Apparently, there was no magic involved. The HIE made consent forms available at hospitals and doctors’ offices throughout its network, as well as making the forms available for download at whyhealthelink.com. (It may also have helped that they can be downloaded in any of 12 languages.)

I downloaded the consent form myself, and I must say it’s not complicated.

Patients only need to fill out a single page, which gives them the option to a) permit participating providers to access all of their electronic health information via the HIE, b) allow full access to the data except for specific participants, c) permit health data sharing only with specific participants, d) only offer access to their records in an emergency situation, and e) forbid HIE participants to access their health data even in the case of an emergency situation.

About 95% of those who consented chose option a, which seems a bit remarkable to me. Given the current level of data breaches in news, I would’ve predicted that more patients would opt out to some degree.

Nonetheless, the vast majority of patients gave treating providers the ability to view their lab reports, medication history, diagnostic images and several additional categories of health information.

I wish I could tell you what HEALTHeLINK has done to inspire trust, but I don’t know completely. I suspect, however, that provider buy-in played a significant role here. While none of this is mentioned in the HIE’s press release or even on its website, I’m betting that the HIE team did a good job of firing up physicians. After all, if you’re going to pick someone patients would trust, physicians would be your best choice.

On the other hand, it’s also possible patients are beginning to get the importance of having all of the data available during care. While much of health IT is too abstruse for the layman (or woman), the idea that doctors need to know your medical history is clearly beginning to resonate with your average patient.

The Subtle Signs of Sepsis Infographic

Posted on September 27, 2017 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.

Sepsis has been a major challenge in healthcare for a long time. This was highlighted really well on the Wolters Kluwer Nursing Center website:

Throughout my experience in health care over the past 30 plus years, the diagnosis of sepsis has been one of the most challenging. Sepsis affects millions of people worldwide and one in four of the people affected will die. The way we recognize and treat sepsis has changed over the years, and in January 2017, the International Guidelines for Management of Sepsis and Septic Shock: 2016 was published. This update to the 2012 guidelines, emphasizes that patients with sepsis should be viewed as having a medical emergency, necessitating urgent assessment and treatment.

According to the Advisory Board, the average direct cost per case for a primary sepsis diagnosis is $18,700, yet the typical Medicare reimbursement for sepsis and sepsis with complications is just $7,100-12,000. It’s no wonder so many hospitals are worried about sepsis.

I’ve been impressed with the way technology has been used to address the problem of Sepsis. I’ve seen a lot of companies working to use analytics to predict sepsis or identify it in real time as it’s happening. I recently saw where Wolters Kluwer partnered with Vocera to be able to connect the Sepsis risk analysis data with the providers, carrying Vocera badges, who can make the proper diagnosis and start treatment in the early stages when Sepsis is most treatable.

This kind of collaboration between healthcare IT vendors is the only way we’re going to make a dent in major healthcare problems like Sepsis. So, I applaud these two companies for working together.

For those that don’t know, September is Sepsis Awareness Month. As part of this month long recognition of Sepsis, Wolters Kluwer put together an infographic that shows the subtle signs of sepsis. While technology can certainly help with Sepsis identification and treatment, there’s still an important human element as well. This infographic highlights the signs that healthcare providers can and should look for and methods of treatment.

What efforts have you seen effective in identifying and treating sepsis in your healthcare organization?

Condition Management vs. Episodic Care Management – #HITsm Chat Topic

Posted on September 26, 2017 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.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 9/29 at Noon ET (9 AM PT). This week’s chat will be hosted by Brian Eastwood (@Brian_Eastwood) from @ChilmarkHIT on the topic of “Condition Management vs. Episodic Care Management.”

The status quo of episodic care management – static care plans that rely heavily on high-touch interventions – is unsustainable if the healthcare industry truly intends to lower costs and improve outcomes. In response, the industry has seen growing interest in digital chronic condition management programs that take a more holistic and individualized approach to care. By and large, these programs use short but frequent interventions that aim to address the root causes of a condition, rather than just the symptoms themselves, in an effort to help program participants manage their condition(s) and improve their quality of life.

Given the current market, health insurers and employers are more interested in adopting condition management programs that provider organizations. This isn’t surprising – employers and insurers have clear “skin in the game” to cut costs, and providers do not – but it is nonetheless disappointing. This week’s #HITsm chat will discuss what it will take for condition management programs to gain the interest of those who deliver care, not just those who pay for care.

References:

  • Assessing the Growing Market for Condition Management Solutions: Blog post and webinar
  • Condition Management: A Healthcare Disruption That Just Might Stick: Blog post

Here are the questions that will serve as the framework for this week’s #HITsm chat:
T1: What are the key drawbacks to static condition mgmt. (both tech and workflow)? #HITsm

T2: What does holistic condition mgmt. do that episodic care mgmt. doesn’t? #HITsm

T3: How can condition mgmt. show that it’s not just a “wellness program” in new clothes? #HITsm

T4: Aside from payment reform, what will it take for provider orgs to pivot to condition mgmt.? #HITsm

T5: Where do you see the market for holistic condition mgmt. in the next 3 years? #HITsm

Bonus: Which vendors or orgs are getting condition mgmt. right? #HITsm

Upcoming #HITsm Chat Schedule
10/6 – After Death Data Donation – A #hITsm Halloween Horror Chat
Hosted by Regina Holliday (@ReginaHolliday), Founder of #TheWalkingGallery

10/13 – Role of Provider Engagement for Improving Data Accuracy
Hosted by @CAQH

10/20 – Community Sharing Chat
Hosted by the #HITsm Community

10/27 – Aggregating the Patient Perspective and Incorporating It Into Software to Change Healthcare
Hosted by Lisa Davis Budzinski (@lisadbudzinski)

We look forward to learning from the #HITsm community! As always, let us know if you’d like to host a future #HITsm chat or if you know someone you think we should invite to host.

If you’re searching for the latest #HITsm chat, you can always find the latest #HITsm chat and schedule of chats here.

Translating Social Determinants of Health Into Clinical Action

Posted on September 25, 2017 I Written By

The following is a guest blog post by Anton Berisha, MD, Senior Director, Clinical Analytics and Innovation, Health Care, LexisNexis Risk Solutions.
The medical community recognizes the importance of social determinants of health (SDOH) – social, economic and environmental conditions in which people are born, grow, live, work and age that impact their health – as significant and direct risk factors for a large number of health care outcomes.

The negative outcomes include stress, mental health and behavioral disorders, alcoholism and substance abuse, to name a few. Negative SDOH worsen a slew of major chronic conditions, from hypertension and Coronary Artery Disease to obesity; they also lead to lower patient engagement and medication adherence while increasing low-intensity ER visits and hospital admissions and readmissions.

In fact, a study shows that medical care determines only 20% of overall health outcomes while social, economic and environmental factors determine about 50% of overall health. The National Quality Forum, Centers for Disease Control and Prevention and World Health Organization have all acknowledged the importance of addressing SDOH in health care.

Not all SDOH are “created equal”

When it comes to SDOH, there is a misconception that all data regarding a person’s lifestyle, environment, situation and behaviors relate to their health. Although there is a myriad of basic demographic data, survey data and other Electronic Health Records (EHR) data available to providers today, much of it has a limited potential for identifying additional health costs and risks.

The key to addressing SDOH is to use current, comprehensive and longitudinal data that can be consistently linked to specific patient populations and provided in a standardized format. One example is attributes derived from public records data such as proximity to relatives, education, income, bankruptcy, addresses and criminal convictions.

Moreover, each SDOH attribute has to be clinically validated against actual healthcare outcomes. Clinically validating attributes is critical to successful predictive analytics because some attributes do not correlate strongly to health outcomes.

For example, while knowing how close an individual’s nearest relative or associate lives to the patient does correlate to health outcomes; knowing how many of those relatives or associates have registered automobiles does not. Even when attributes are clinically validated, different attributes correlate to different outcomes with different accuracy strengths.

Translating SDOH into actionable intelligence

After SDOH have been correlated to healthcare outcomes, providers have two implementation options. One is to use relevant individual SDOH attributes per outcome in clinical and analytic models to better assess and predict risk for patients. Another is to use SDOH as part of risk scores estimating specific healthcare risks; for e.g., to estimate an individual’s total health care risk over the next 12 months based on cost; a 30-day readmission risk; or a patient engagement score.

Risk estimation can be done either in combination with other types of legacy healthcare data, such as claims, prescription and EHR data or with SDOH alone, in the absence of medical claims.

Recently, a client of LexisNexis® Health Care did an independent study to evaluate the impact and usefulness of Socioeconomic Health Score (SEHS) in risk assessment for several key chronic conditions, when no other data are available. Findings proved that the top decile of SEHS captures significantly more members with given conditions than the bottom decile. The study concluded that the difference was important and very helpful in estimating risks in a newly acquired population without legacy healthcare data.

Integrating SDOH into clinical workflows and care recommendations

Validated SDOH can be presented in a form of risk drivers or reason codes directing the clinician toward the most important factors influencing a given negative outcome for each patient: income, education, housing or criminal records.

The risk drivers and reason codes can then be integrated into workflows within the clinician’s IT systems, such as the EHR or care and case management, in the form of an easy-to-understand presentation. It could be a data alert that is customizable to patients, treatments and conditions, helping the provider make score-based decisions with greater accuracy and confidence. At this point, the SDOH information becomes actionable because it has the following characteristics:

  • It is based on hard facts on every individual.
  • It is based on correlation and statistical significance testing of large pools of patients with similar behavior.
  • It provides clear and understandable reason codes driving the negative outcomes.
  • It can be tied to intervention strategies (outlined below) that have demonstrated positive results.

Clinicians empowered with actionable SDOH information can modify their interventions and follow-up strategies accordingly. Based on resources at hand, patients living in negative SDOH could be either properly managed by clinicians themselves or other medical staff, social workers and newly created roles such as health coaches. Sub-populations at risk could benefit from access to community resources to get help with housing (permanent supportive housing for homeless), transportation, education, childcare and employment assistance.

Moreover, SDOH are particularly effective in helping providers develop a population health management strategy fueled by prioritized tactics for preventive care. Tactics can range from promotion of healthy food to free screening services. For patients with chronic diseases (who can typically be managed appropriately when they adhere to therapy and healthy lifestyle choices), SDOH-informed interventions can help keep them under control and potentially reduce severity. For patients recently released from the hospital, aftercare counseling could prevent complications and readmissions.

To sum it up

Socioeconomic data is a vital force for healthcare risk prediction as it provides a view into the otherwise hidden risks that cannot be identified through traditional data sources. When SDOH are clinically validated and correlated to healthcare outcomes, they help providers better understand an individual’s risk level and address it through appropriate intervention strategies.