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Payers Say Value-Based Care Is Lowering Medical Costs, But Tech Isn’t Contributing Much

Posted on June 22, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A new survey of health insurers has concluded that while value-based care seems to be lowering healthcare costs significantly, they aren’t satisfied with the tools they have to analyze value-based performance.

The report, which draws on a survey sponsored by Change Healthcare, including answers from 120 payers across several types of insurance, including managed Medicare, managed Medicaid and commercial plans.

The topline finding from the report was that value-based care (VBC) has lowered healthcare costs by 5.6% on average, with one-quarter of respondents reporting savings of more than 7.5%.

Meanwhile, the volume of fee-for-service payments has dropped dramatically as a percent of overall payments, now accounting for just 37.2% of all reimbursement among respondents. That number is expected to fall below 26% by 2021.

Not only that, 64% of payers said that provider relationships improved, and 73% said patient engagement improved. This suggests that providers have made some strides in delivering value-based care, as many had a hard time restructuring their business in the past.

That said, some payers haven’t met their own VBC goals. In particular, 66% of payers are investing administrative staffers to support episode-of-care programs given what the study terms “exceptional” medical cost savings. Also, one third to one-half said that episode-of-care models were either very or extremely effective at improving care quality.

However, payers haven’t made much progress as they’d like in rolling out episode-of-care programs. While 21% of payers said they were capable of rolling out a new episode-of-care program in 3 to 6 months, more than a third said the needed a year to launch such a program, 21% said it would take 18 months, and 13% said it would take up to 24 months or more. In other words, many payers are so far behind the curve that the programs they’re designing might be obsolete by the time they roll them out.

What’s more, they’ve had a tough time getting providers interested in episode-of-care programs. Forty-three to 58% reported that it is either very or extremely difficult to get providers to participate in these efforts. Not only that, even when they find interested providers, payers are having a hard time finding common ground with them on episode definitions, budgets, the details of risk and reward sharing and performance metrics. These disagreements could prove a major hurdle to overcome.

In addition, more than half of payers said they were not very satisfied with the current value-based analytics, automation and reporting tools, even though most of the tools were developed in-house by the payers themselves. It could be that given provider resistance, the payers aren’t quite sure about what to look for. Regardless, it seems that payers have a longer-than-expected road to travel here.

Geisinger, Penn State Researchers Predict Risk Of Rehospitalization Within Three Days Of Discharge

Posted on June 15, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In recent times, healthcare organizations have focused deeply on the causes of patient readmissions to the hospital. It’s a problem that affects both physicians and health systems, particularly if the two are not in synch.

To date, providers have focused on readmissions happening within 30 days, largely in an effort to avoid financial penalties imposed by Medicare and Medicaid. However, if the following research is solid, it could push the focus of care much closer to hospital discharge dates.

In an effort which could change the process of avoiding readmissions, a group of researchers has found a way to predict a patient’s risk for needing additional medical care within three days of discharge. The new approach developed jointly by Penn State and Geisinger Health Plan, relies on clinical, administrative and socio-economic data drawn from patients admitted to Geisinger over two years.

The model they created is known as REDD, an acronym which stands for readmission, emergency department or death. Using this model can help physicians target interventions effective and reduce the number of adverse events, according to Deepak Agrawal, one of the Penn State researchers.

You won’t be surprised to hear that readmissions after 30 days are often related to social determinants of health, such as a poor home environment, limited access to services and scant social support. Providers are certainly working to close these gaps, but to date, this has remained a major challenge.

However, the dynamics are different when finding patients who may be readmitted quickly. “Readmissions closer to discharge are more likely to related to factors that are actually present but are not identified at the time the patient is discharged,” said research team leader Sundar Kumara, Allen E. Pearce and Allen M. Pierce Professor of Industrial Engineering with Penn State, who was quoted in a prepared statement.

Another Penn State researcher, Cheng-Bang Chen, added another interesting observation. He noted that the more time that passes after a patient gets discharged, the less likely it is that problems will be caught in time. After all, it may be a while before treating physicians have time to review lengthy hospital records, and the patient could experience a time-sensitive event before the physician completes the review.

To test the REDD program, Geisinger ran a six-month pilot tracking high-risk patients and adding additional services designed to avoid readmissions, ED visits or death.

To treat this population effectively, physicians took a number of steps, such as scheduling appointments with patients’ primary care doctors, educating patients about their medications and post-discharge care plans,  having the inpatient clinical pharmacist review the provider’s recommendations, filling patient prescriptions before discharge and having the hospital check on patients discharged to a skilled nursing facility one day after discharge.

It’s worth noting that there was one major issue which undermined the research results. Penn State reported that because of a shortage of nurses at the hospital during the pilot, they couldn’t tell whether the REDD program met its goals.

Still, researchers are convinced they’re heading in the right direction. “If the REDD model was fully implemented and aligned with clinical workflows, it has the potential to dramatically reduce hospital readmissions,” said Eric Reich, manager of health care re-engineering at Geisinger.

Let’s hope he’s right.

AI Tool Helps Physician Group Manage Prescription Refills

Posted on April 25, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Most of the time, when we hear about AI projects people are talking about massive efforts spanning millions of records and many thousands of patients. A recent blog item, however, suggests that AI can be used to improve comparatively modest problems faced by physician groups as well.

The case profiled in the blog involves Western Massachusetts-based Valley Medical Group, which is using machine learning to manage medication refills. The group, which includes 115 providers across four centers, implemented a product known as Charlie, a cloud-based tool made by Healthfinch 18 months ago. (I should note, at this point, that the blog maintained is by athenaHealth, which probably has a partnership with Healthfinch. Moving on…)

Charlie is a cloud-based tool which automates the process of prescription refills by integrating with EHRs. Charlie processes refill requests much like a physician or pharmacist would, but more quickly and probably more thoroughly as well.

According to the blog item, Charlie pulls in refill requests from the practice’s EHR then adds relevant patient data to the requests. After doing so, Charlie then runs the requests through an evidence-based rules engine to detect whether the request is in protocol or out of protocol. It also detects duplicates. errors and other problems. Charlie can also absorb specific protocols which let it know what to look for in each refill request it processes.

After 18 months, Valley’s refill process is far more efficient. Of the 10,000 refill requests that Valley gets every month, 60% are handled by a clerical person and don’t involve a clinician. In addition, clerical staff workloads have been cut in half, according to the practice’s manager of healthcare informatics.

Another benefit Valley saw from rolling out Charlie with that they found out which certain problems lay. For example, practice leaders found out that 20% of monthly refill requests were duplicate requests. Prior to implementing the new tool, practice staff spent a lot of time investigating the requests or worse, filling them by accident.

This type of technology will probably do a lot for medium-sized to larger practices, but smaller ones probably can’t afford to invest in this kind of technology. I have no idea what Healthfinch charges for Charlie, but I doubt it’s cheap, and I’m guessing its competitors are charging a bundle for this stuff as well. What’s more, as I saw at #HIMSS18, vendors are still struggling to define the right AI posture and product roadmap, so even if you have a lot of cash buying AI is still a somewhat risky play.

Still, if you’re part of a small practice that’s rethinking its IT strategy, it’s good to know that technologies like Charlie exist. I have little doubt that over time — perhaps fairly soon — vendors will begin offering AI tools that your practice can afford. In the meantime, it wouldn’t hurt to identify processes which seem to be wasting a lot of time or failing to get good results. That way, when an affordable tool comes along to help you’ll be ready to go.

Self-Learning Analytics and Making Analytics Useful

Posted on April 2, 2018 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.

One of the shocks to me at HIMSS 2018 was that there wasn’t nearly as much discussion around healthcare analytics as I thought there would be. I thought for sure we’d see an explosion of proven analytics that healthcare organizations could start to take advantage of. Maybe I just missed it, but I certainly didn’t see anything all that new.

It’s too bad because that’s one of the huge opportunities I see for healthcare. I was looking through some old notes from conferences and saw a note where I wrote: “What you do with the data is the competitive differentiator, not the data.

Certainly, you need access to the data to be successful, but there are a lot of organizations out there which have access to health data and they’re not making any sort of dent. Many of the now defunct HIEs had access to the data, but they didn’t know what to do with all that data. I’m still on the search for more analytics which are useful.

One other idea I found in my notes was the concept of a self-learning analytic. Related to this was the discussion we had about black box analytics in a recent #HITsm Twitter chat. I don’t think they have to be the same, but I do think that the key to successful healthcare analytics is going to require some component of self-learning.

The concept is simple. The analytic should look at its past recommendations and then based on the results of past recommendations, the analytic should adjust future recommendations. Notice that I still call it recommendations which I think is still the right approach for most analytics. This approach to constantly learning and evolving analytics is why it’s so hard to regulate healthcare analytics. It’s hard to regulate moving targets and a self-learning analytic needs to be moving to be most effective.

This is possibly why we haven’t seen an explosion of healthcare analytics. It’s hard to get them right and to prove their effectiveness. Plus, they need to continually evolve and improve. That’s the opposite of what researchers want to hear.

This is why the future of healthcare analytics is going to require deep collaboration between healthcare analytics vendors and provider organizations. It’s not a black box that you can buy and implement. At least not yet.

What’s been your experience with healthcare analytics? Where are you seeing success? We’d love to hear your thoughts in the comments.

Supercharged Wearables Are On The Horizon

Posted on January 3, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Over the last several years, the healthcare industry has been engaged in a rollicking debate over the value of patient-generated health data. Critics say that it’s too soon to decide whether such tools can really add value to medical care, while fans suggest it’s high time to make use of this information.

That’s all fine, but to me, this discussion no longer matters. We are past the question of whether consumer wearables data helps clinicians, which, in their current state, are under-regulated and underpowered. We’re moving on to profoundly more-capable devices that will make the current generation look like toys.

Today, tech giants are working on next-generation devices which will perform more sophisticated tracking and solve more targeted problems. Clinicians, take note of the following news items, which come from The New York Times:

  • Amazon recently invested in Grail, a cancer-detection start-up which raised more than $900 million
  • Apple acquired Beddit, which makes sleep-tracking technology
  • Alphabet acquired Senosis Health, which develops apps that use smartphone sensors to monitor health signals

And the action isn’t limited to acquisitions — tech giants are also getting serious about creating their own products internally. For example, Alphabet’s research unit, Verily Life Sciences, is developing new tools to collect and analyze health data.

Recently, it introduced a health research device, the Verily Study Watch, which has sensors that can collect data on heart rate, gait and skin temperature. That might not be so exciting on its own, but the associated research program is intriguing.

Verily is using the watch to conduct a study called Project Baseline. The study will follow about 10,000 volunteers, who will also be asked to use sleep sensors at night, and also agreed to blood, genetic and mental health tests. Verily will use data analytics and machine learning to gather a more-detailed picture of how cancer progresses.

I could go on, but I’m sure you get the point. We are not looking at your father’s wearables anymore — we’re looking at devices that can change how disease is detected and perhaps even treated dramatically.

Sure, the Fitbits of the world aren’t likely to go away, and some organizations will remain interested in integrating such data into the big data stores. But given what the tech giants are doing, the first generation of plain-vanilla devices will soon end up in the junk heap of medical history.

Capturing Unstructured Data for Better Patient Care

Posted on October 9, 2014 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.

The following is a guest blog post by Dr. Chris Tackaberry, CEO of Clinithink.
Clinithink_Chris Tackaberry_CEO and Founder
There is a veritable gold mine of high value data locked inside the free text fields of all EHR systems, as well as in the free text of other sources of clinical documentation such as progress notes, discharge summaries, consult requests and diagnostic reports. In all of these sources, rich, actionable patient data is trapped in unstructured text—stored side by side with more easily accessible structured data.

Take echocardiography reports, for example. The data contained within them—specifically ejection fraction, for instance—are crucial to the management of heart failure as outlined within NQMC core measures for this serious chronic condition. Yet seldom is an ejection fraction captured as structured data. Instead, it is usually documented as free text.

Since narrative has been an inherent part of clinical workflow for many years, HIT software vendors have reasonably added free text fields to their applications. While there is clearly value in driving insights from structured data captured in such systems, the unstructured piece in free text fields remains untapped. This represents a source of potentially significant additional value that can be gleaned from EHR and other clinical documentation sources. However, conventional structured data tools do not support the ability to exploit it for use in clinical decision making.

Unlocking the clinical value in unstructured data

In the days of paper charts, highly experienced physicians were able to quickly scan large charts to find information such as allergies, medications, family history, past and current symptoms, social history, and other background detail that provided the context so critically important to any clinical encounter. This information was usually summarized in documents (discharge summaries, referrals, etc.). Ironically, such information is now more difficult to find when stored electronically.

If the existence of unstructured narrative data were known, discoverable, searchable and actionable for every patient—across any EMR or other health IT systems—the currently hidden additional diagnostic and clinical data could further increase the efficiency and quality of care. Clinical Natural Language Processing (CNLP) is a technology that enables access to unstructured narrative data which can be used to unlock this additional value. Using narrative data found in reports, web pages, transcribed output, EMRs, and other electronic sources of free text at the point of care can expand our knowledge of the patient beyond the information obtained from structured data.

Recently, the AMA issued a report in conjunction with the RAND Corporation on the need for EHR vendors to improve the software solutions they are delivering to better meet the needs of physicians. Utilizing CNLP technology to access the clinical value inherent in unstructured EHR data would allow vendors to begin addressing some of the potential improvements.

As we move from a world in which healthcare is delivered on an episodic basis retrospectively to one where care is delivered almost continuously and prospectively, CNLP increases the opportunity to deliver rich, actionable and meaningful clinical content to help improve decision-making for more accurate, evidence-based and effective care.

Dr. Chris Tackaberry is the CEO of Clinithink.

Will Texans Own Their DNA? Greg Abbott, Candidate for Governor, Thinks They Should

Posted on November 26, 2013 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.

The following is a guest post by Dr. Deborah Peel, Founder of Patient Privacy Rights.

On November 12th, Abbott released his “We the People Plan” for Texas. Clearly he’s heard from Texans who want tough new health data privacy protections.

Topping his list are four terrific privacy recommendations for health and genetic data:

  • “Recognize a property right in one’s own DNA.”
  • “Make state agencies, before selling database information, acquire the consent of any individual whose data is to be released.”
  • “Prohibit data resale and anonymous purchasing by third parties.”
  • “Prohibit the use of cross referencing techniques to identify individuals whose data is used as a larger set of information in an online data base.”

The federal Omnibus Privacy Rule operationalized the technology section of the stimulus bill. It also clarified that state legislatures can pass data privacy laws that are stronger than HIPAA (which is a very weak floor for data protections).

Texans would overwhelmingly support the new state data protection laws Abbott recommends . If elected, hopefully Abbott would also include strong enforcement and penalties for violations. Contracts don’t enforce themselves. External auditing and proof of trustworthy practices should be required.

Is this the beginning of a national trend?  I think so. The more people know about today’s health IT, the more they will reject electronic systems and data exchanges designed for the hidden use and sale of sensitive personal health data.

Show Me the Data Video

Posted on May 6, 2013 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.

Paul Levy blogged about Eric Topol’s move to be editor-in-chief at Medscape. It’s an interesting move for Dr. Topol and it will be interesting to see how Medscape does with Dr. Topol at the helm. However, even more entertaining is the video that Paul Levy embedded in his post. A nice rework of the popular scene from Jerry Maguire. Many of my readers will really appreciate it. I guess Dr. Topol uses this video in the lectures he does to explain why patients need their data. Enjoy!

The Body as a Source of Big Data Infographic

Posted on March 1, 2013 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.

NetApp recently put out an Infographic that depicts the body as a source of big data. It’s a pretty cool representation of the power of data in healthcare. In my HIMSS 2013 preview video, I suggested that HIMSS 2013 might be the year of Healthcare Big Data at HIMSS. This infographic displays many of the opportunities.
The Power of Healthcare Data