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AI Making Doctors Better Is the Right Approach

Posted on September 6, 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.

This summer Bertalan Meskó, MD, PhD posted 10 ways that AI (Artificial Intelligence) could make him a better doctor. Here are his 10 ways:

1) Eradicate waiting time
2) Prioritize my emails
3) Find me the information I need
4) Keep me up-to-date
5) Work when I don’t
6) Help me make hard decisions rational
7) Help patients with urgent matters reach me
8) Help me improve over time
9) Help me collaborate more
10) Do administrative work

This is a great list of ways that AI can make doctors better. No doubt there are even more ways that we’ll discover as AI continues to improve. However, I love this list because it looks at AI from the appropriate point of reference. AI is going to be something that improves the doctor, not replaces the doctor.

I know that many people have talked about how technology is going to replace the doctor. The most famous of which is Vinod Khosla. However, you have to remember that Vinod is an investor and so he needs to drum up companies with ambitious visions. I believe his statement was as much about finding companies that will push the bounds of healthcare as much as it was his prediction for the future of healthcare. However, it no doubt created a lot of fear for doctors.

The reality is that some aspects of what a doctor does will be replaced by technology, but as the list above illustrates, that can be a very good thing for doctors.

AI is coming to healthcare. In some ways, it’s already here. However, the AI that’s coming today isn’t about replacing the doctor, it’s about making the doctor better. Honestly, any doctor that can’t embrace this idea is a doctor that shouldn’t have a medical license.

Should doctors be cautious in how quickly they adopt the technology and should they take the time to make sure that the AI won’t have adverse impacts on their patients? Absolutely. However, there’s a tipping point where not using AI is going to be much more damaging to patients than the risks that are likely to make headlines and scare many. Doctors need to be more driven by what’s best for their patients than fear inducing headlines.

AI will make doctors lives better in a wide variety of ways. It won’t replace the doctor but will enhance the doctor. That’s exciting!

Number Of Healthcare AI Investments Climbing Rapidly

Posted on August 31, 2017 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.

I’ve written frequently about the growing influence of artificial intelligence tools on healthcare delivery. These include not only support for advanced analytics and adaptive processes but also a growing number of clinically-oriented chatbots.

As far as I knew, these trends were early in their lifecycle, and ventures dipping their toes into healthcare AI were still just dots on a map. Apparently, I was way off on this one.

According to a recent article from CB Insights, healthcare has been, and continues to be, the top industry for AI investment deals. According to the company, there were 29 venture capital investments in healthcare AI last quarter, and from what analysts are saying, that number may rise substantially over the next few quarters. In fact, analysts noted that as of late August, it looked like this quarter’s level of healthcare AI deals would beat the previous quarter’s results.

Just to be clear, CB Insights’ definition of “healthcare AI” covers a lot of ground. The firm defines AI in healthcare as occurring when startups leverage machine learning algorithms to reduce drug discovery times, provide virtual assistance to patients or improve the accuracy of medical imaging and diagnostic procedures – plus some additional unspecified additional applications. (Its list does exclude hardware-focused robotics startups and health-related AR/VR ventures.)

Still, even if you peel away the drug discovery, research and diagnostics investments, there’s plenty of VC deals to track. For example, UK-based Babylon Health raised $60 million in funding the past quarter, the largest funding round tracked by CB Insights. Perhaps this is less surprising given that Babylon Health’s first VC deal included money from Alphabet’s DeepMind Technologies, a nice pedigree for any startup, but it’s still a huge deal. (As you’ll see if you click the link, DeepMind has plenty of healthcare IT development of its own going on.)

Other interesting funding deals included investments in mental health startup Spring Health and risk analytics company OM1, which snagged $15 million in Series A funding. Also, CB Insights found that while most deals involved US companies, four healthcare AI investments went to companies in India and three to companies in China.

Having absorbed this data, I’m eager to see whether my pet interest makes it onto CB Insights’ radar for Q3 of this year. You may already have a general idea about how AI is being deployed in predictive analytics for use in clinical care improvement, or to increase researchers’ ability to pinpoint genes for precision medicine projects, but you may not be aware that another hot application for AI use in healthcare is to provide counseling (and perhaps, in the future, psychiatric services) via chatbot.

I find these services particularly interesting because psychotherapy via AI has some characteristics which differentiate it from many other forms of AI-driven clinical options. One standout is that people may actually tell a chatbot more than they will a live person in some cases, which makes such bots helpful in supporting populations (such as soldiers with PTSD) which might be unlikely to open up otherwise. Let’s see if such applications attract big VC investors anytime soon.

3 CEO Perspectives on Medication Adherence – Part 3 of 3

Posted on August 25, 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.

KNB Communications recently interviewed three leaders in healthcare technology – Propeller Health Co-founder and CEO David Van Sickle, RxAnte Founder and CEO Dr. Josh Benner, and RxREVU Founder and CEO Carm Huntress – to get their insights on medication adherence.

This is part 3 in a 3 part interview on medication adherence. Be sure to read part 1 and part 2 as well.

Q: How can technology be harnessed to collect patient-reported outcomes such as real-time symptoms and perceptions of medications?

DVS: Propeller sensors passively collect information about use of inhaled medications and transmit that information through a smartphone to Propeller. Then, the system tries to make sense of how the person is doing, to estimate their level of risk and impairment, and to report back its impression and suggestions through digital apps and interfaces.

We ask people to add details about their symptoms, tell us what they perceived to have triggered their episode, and to answer periodic questionnaires that provide other kinds of information, such as whether they are waking up at night. Altogether, this information teaches us a lot about how asthma is affecting that individual in their daily life and how they are responding to treatment.

With this combination of self-reported information and medication use data, Propeller is able to inform physicians about which of their patients need more attention, to help them better understand what might need adjustment to gain control of the symptoms, and to encourage collaborative efforts to improve its care and treatment.

JB: I think this concept has a lot of merit in managing adherence, because if we can get people to communicate with us about how they’re using their medicine and how their medicines are making them feel in real-time, then we can more actively detect and overcome those barriers to nonadherence before they become a decision to stop the medicine.

Phone calls and mobile apps alike can be used to collect information from patients, assess how the medication is working, and tailor the intervention program.  For example, we use our live pharmacist call center to collect patient-reported outcomes and potential reasons why patients may have trouble using their medications as prescribed.  Response rates to digital approaches are typically lower, but they are also less costly.

CH: This is a critical issue because most technologies today, especially electronic health record systems, aren’t really set up to store anything beyond basic patient clinical factors. We need a lot more technology today that can go beyond these basic factors.  We need to think about socioeconomic, patient-reported outcome measures, and other factors to really improve our understanding of medication adherence. How effective a medication is, what outcome it’s really delivering for certain types of patients, and really looking at technologies that are sophisticated decision support systems that capture all this at the point of care, similar to what we’re doing at RxREVU, with our prescription decision support platform, and capturing those key socioeconomic factors.

If we know the patient has a poor adherence, why is that happening and is it a side effect? Is it a socioeconomic factor? What are those patient-reported outcome measures we can capture and store and then longitudinally feed that data across a whole host of patients to better understand how those factors are affecting adherence?

At the patient level, we’re looking at really simple technologies today, Even text messaging is a great solution, especially for many low-income patients that may not have a smartphone, to engage with them and capture that information.

We don’t need sophisticated apps yet. We’re not there at all, in terms of capturing these types of measurements. It’s really about these simple technologies that can engage a patient with a simple question, allow them to answer that through a technology like SMS, and then obviously store that information and make it available to stakeholders to evaluate and better understand adherence issues. Those are definitely some things I think about, as we start to get better at capturing patient-reported outcomes measures that directly affect adherence.

Additional Comments

JB: There’s an exciting tidal wave of interest in the topic of medication adherence across the healthcare system. Fifteen years ago, pharmaceutical companies were the only ones investing heavily in medication adherence. But this has changed dramatically, especially over the past six to seven years.

It’s changed because of new evidence that helped us better understand the consequences of non-adherence as a population health management problem. This stimulated the development of consensus-based quality measures for medication use.

Today, health plans, providers, pharmacies and pharmacy benefit management companies are increasingly being compensated based on the quality of care they deliver—and that is an incentive to improve adherence to critical medications. RxAnte’s products and services are used by all of these stakeholders—and in the years ahead, we want to facilitate unprecedented collaboration among these parties to help patients get more from medicines.

CH: In terms of adherence, we at RxREVU really take a different point of view. Many companies are focused clearly on the patient’s experience and around adherence and how they improve that. But ultimately, all these decisions start at the point of care. We are solely focused on helping the provider at the point of care make the most informed decision that’s going to drive an appropriate prescription to the patient, that they can afford, and they can adhere to.

As we look to the future, I think this is a critical piece that we need more and more technologies at the point of care supporting clinician’s decisions, because ultimately, you as the patient aren’t making the decision; your provider is. That’s sometimes missed, and providers are a key component to the decision-making. It is really is a shared decision-making and technologies that can sit alongside those patients and providers in the exam room and support those decisions are really going to be critical in the coming years.

This was part 3 in a 3 part interview on medication adherence. Be sure to read part 1 and part 2 to read the full interview.

About David Van Sickle

David Van Sickle is co-founder and CEO of Propeller Health – the leader in respiratory digital health. David received his PhD in medical anthropology. His dissertation research, funded by the National Science Foundation, examined the rising prevalence of asthma and allergy in India. He was then an Epidemic Intelligence Service officer at the Centers for Disease Control and Prevention in Atlanta, where he was assigned to the Air Pollution and Respiratory Health Branch. During this time, he provided epidemiological support to the National Asthma Control Program, and investigated the health effects of a variety of environmental exposures. In addition, he helped establish emergency illness and injury surveillance in coastal Mississippi after Hurricane Katrina. David was also named a Champion of Change by the White House for his work on innovation.

About Josh Benner

A leading voice on medication adherence, Dr. Benner’s award-winning research and numerous publications have shed new light on the problem of nonadherence and identified promising approaches to improving it.  He is the founder and CEO of RxAnte, the leading provider of predictive analytics and targeted clinical programs for improving medication use.

Before joining RxAnte, Dr. Benner was Fellow and Managing Director at the Brookings Institution’s Center for Health Care Reform, where he focused on medical technology policy.

Prior to Brookings, Dr. Benner was principal at ValueMedics Research, an analytic and consulting services firm. Following the acquisition of ValueMedics by IMS Health in 2007, he served as senior principal in health economics and outcomes research and global lead for medication adherence at IMS. Dr. Benner received his Doctor of Pharmacy degree from Drake University and his Doctor of Science in health policy and management from the Harvard University School of Public Health.

About Carm Huntress

Carm Huntress is an entrepreneur and strategic leader with over 20 years of experience in startups focused around consumer and enterprise technology. His first web development and hosting company he started while in high school was eventually acquired in 2001.  After finishing his degree in electrical engineering at Northeastern University in 2004, he went on to work for PlumVoice, an IVR and voice technology startup, where he ran their network operations.  He later was asked to run product development at My Perfect Gig, a Northbridge and Commonwealth Venture start-up.

After two years as CTO at Reef Partners, where he ran the technology for a number of portfolio companies, he became CTO at Audiogon.com, the largest high end audio site in the world.  He managed the transition of the core technology platform and team for growth.  In 2013 he moved to Denver where he founded RxREVU.

3 CEO Perspectives on Medication Adherence – Part 2 of 3

Posted on August 24, 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.

KNB Communications recently interviewed three leaders in healthcare technology – Propeller Health Co-founder and CEO David Van Sickle, RxAnte Founder and CEO Dr. Josh Benner, and RxREVU Founder and CEO Carm Huntress – to get their insights on medication adherence.

This is part 2 in a 3 part interview on medication adherence. Be sure to read part 1 and part 3 as well.

Q: A recent study by the Journal of the American Board of Family Medicine underscores the emotional and behavioral barriers faced by patients with chronic illnesses. How do we leverage technology to reduce the impact of social and economic factors such as poverty, transportation challenges, and medication costs?

DVS: We know that, in some cases, people avoid taking their daily medicines for asthma because they’re costly and they can’t afford the required co-pay or co-insurance amounts. But without those medications, people are more likely to develop symptoms, and may have to spend quite a lot of their discretionary income dealing with the costs of uncontrolled asthma, whether it’s because they’re forced to miss a day of work or school, or because they need to seek medical attention. The faster we’re able to help bring someone’s disease under control, the sooner they can avoid unnecessary costs and suffering.

We know that a lot of things in the environment, such as workplace exposures or air pollution in the community, can have a material effect on a person with chronic respiratory disease. By learning about the locations where people have symptoms, Propeller aims to help them avoid or mitigate those exposures.

For the past few years, Propeller has been part of program in Louisville to help residents better manage their asthma and to collect information about where, when and among whom asthma is happening across the metro. Aggregate data from thousands of participants has highlighted how socioeconomic context contributes to poor respiratory health. At the same time, by making these patterns visible, we’ve also opened new opportunities for municipal discussion, policy decisions, and applied public health interventions to try to address these risk factors, and to increase the respiratory health of the entire community.

JB: These are really important barriers. The cost of medications is going up. That makes them unaffordable for some patients, and those are often the patients that are also most vulnerable to the consequences of non-adherence—like low-income and older Americans for whom these medications are really important to them staying out of the hospital, keeping a job, or otherwise living independently. So, we are increasingly doing work in the Medicare, Medicaid and dual-eligible populations. We use advanced predictive analytics to identify members of those populations who are at risk, which means we predict whether they’ll be able to be adherent to the medications that have been prescribed for them.

More than that, we also predict the consequences of their expected adherence. For example, we’ll predict what their non-adherence is likely to cost over the next year or two. That score enables us to prioritize members of those populations tailor programs to the patients who are most likely to benefit.  Another technology we developed, RxEffect, allows us to deliver this information in prioritized workflows to physician offices or care managers, so they always know in real time which of their patients need their attention and what problems to focus on.

Different interventions can solve for different barriers. We use telephone outreach with interactive voice response, because that’s an effective way to facilitate a refill for a patient. On the more intensive end of the spectrum, we use live pharmacist care managers to make sure that they understand the importance and the benefits of remaining adherent and to troubleshoot drug therapy problems that the patient might report.  If they say, “I’m having a side effect with this med,” or, “I can’t afford this med,” the pharmacist is able to go back to the patient’s prescriber, get it changed to something that the patient might find more tolerable or more affordable, and call the patient back to offer to help get that prescription filled.

A third approach is to use so-called “digital therapeutics” or a combination of digital devices and communication tools to maintain long-term engagement with the patient. These services can be delivered through mobile phones like secure text messaging and secure chat to create an ongoing dialogue with the individual.  That provides a conduit to deliver things like a video on how to use your asthma inhaler correctly, so that you and your asthmatic child can use that medicine correctly and stay out of the hospital. Or to deliver a co-pay assistance coupon or some other patient assistance tool provided by the manufacturer to overcome the cost barriers for that particular patient. This is potentially cost-effective and scalable because of the growing mobile and smartphone adoption among these populations.

CH: First and foremost, socioeconomic factors need to be brought into the equation in terms of determining what medication is right for a patient, which will ultimately lead to their adherence. Things we specifically look at are concepts around, for instance, pharmacy deserts, and the challenge for patients, because of public transportation combined with the location of specific pharmacies can lead many patients, especially low-income ones, into situations where they don’t have access to a supermarket or pharmacy within a reasonable distance, that has a pharmacy where they can get their medication.

Another issue is cost and understanding of patients’ income and what they’re ultimately going to be able to afford. Patients won’t take medications or won’t be adherent to medications they can’t afford, period. There’s really no way around that and I think that’s something that is directly tied to socioeconomic factors.

Technologies that can bring those concepts forward and identify those socioeconomic factors up-front and address them in both the exam room as well as the patient’s in a process of getting their medication filled or refilled are going to be critical, because there are a lot of programs that can support patients’ costs. Obviously identify these patients and help support them. There’s a lot of data out there that’s just not being collected and brought together cohesively and made easily accessible to patients to find and access this type of information. I think those are some critical things that really relate to the socioeconomic pieces of adherence.

This was part 2 in a 3 part interview on medication adherence. Be sure to read part 1 and part 3 to read the full interview.

About David Van Sickle

David Van Sickle is co-founder and CEO of Propeller Health – the leader in respiratory digital health. David received his PhD in medical anthropology. His dissertation research, funded by the National Science Foundation, examined the rising prevalence of asthma and allergy in India. He was then an Epidemic Intelligence Service officer at the Centers for Disease Control and Prevention in Atlanta, where he was assigned to the Air Pollution and Respiratory Health Branch. During this time, he provided epidemiological support to the National Asthma Control Program, and investigated the health effects of a variety of environmental exposures. In addition, he helped establish emergency illness and injury surveillance in coastal Mississippi after Hurricane Katrina. David was also named a Champion of Change by the White House for his work on innovation.

About Josh Benner

A leading voice on medication adherence, Dr. Benner’s award-winning research and numerous publications have shed new light on the problem of nonadherence and identified promising approaches to improving it.  He is the founder and CEO of RxAnte, the leading provider of predictive analytics and targeted clinical programs for improving medication use.

Before joining RxAnte, Dr. Benner was Fellow and Managing Director at the Brookings Institution’s Center for Health Care Reform, where he focused on medical technology policy.

Prior to Brookings, Dr. Benner was principal at ValueMedics Research, an analytic and consulting services firm. Following the acquisition of ValueMedics by IMS Health in 2007, he served as senior principal in health economics and outcomes research and global lead for medication adherence at IMS. Dr. Benner received his Doctor of Pharmacy degree from Drake University and his Doctor of Science in health policy and management from the Harvard University School of Public Health.

About Carm Huntress

Carm Huntress is an entrepreneur and strategic leader with over 20 years of experience in startups focused around consumer and enterprise technology. His first web development and hosting company he started while in high school was eventually acquired in 2001.  After finishing his degree in electrical engineering at Northeastern University in 2004, he went on to work for PlumVoice, an IVR and voice technology startup, where he ran their network operations.  He later was asked to run product development at My Perfect Gig, a Northbridge and Commonwealth Venture start-up.

After two years as CTO at Reef Partners, where he ran the technology for a number of portfolio companies, he became CTO at Audiogon.com, the largest high end audio site in the world.  He managed the transition of the core technology platform and team for growth.  In 2013 he moved to Denver where he founded RxREVU.

3 CEO Perspectives on Medication Adherence – Part 1 of 3

Posted on August 23, 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.

Medication adherence has been called the least-appreciated aspect of medicine but its impact is certainly noteworthy.

A recent report, “Adherence and Health Care Cost,” characterized it as “an important public health consideration, affecting health outcomes and overall health care costs” and estimated between 20 and 50 percent of patients are non-compliant with drug therapy.

Additionally, poor medication adherence following hospitalization costs the U.S. healthcare system roughly $100 billion annually, according to a New England Journal of Medicine study.

As our healthcare system moves to value-based reimbursement, technology will help improve medication adherence rates. With this in mind, KNB Communications recently interviewed three leaders in healthcare technology – Propeller Health Co-founder and CEO David Van Sickle, RxAnte Founder and CEO Dr. Josh Benner, and RxREVU Founder and CEO Carm Huntress – to get their insights on this important topic.

This is part 1 in a 3 part interview on medication adherence. Be sure to check back and read part 2 and part 3 once they’re published.

Q: What are some solutions for addressing the top reasons for medication adherence failure:

  • Medications never getting to the patient
  • Medications not being taken correctly
  • Medications not being refilled

DVS: At Propeller, we focus on respiratory disease. We spend all of our time and attention trying to figure out ways to help people with asthma and COPD better and more effectively use the medicines that they take to prevent symptoms from occurring.

These are daily medicines that are subject to factors such as: people forgetting to take them; people not understanding them; people thinking that they’re taking them correctly but not, in fact, doing so; or people trying to negotiate or even intelligently adjust their regimens in one way or another, to benefit their life or for their own understanding of medicine.

Propeller builds devices that capture information about the day-to-day use of those medications and attempts to understand the patterns with which people are taking them or not. Then, we’re able make use of that information through digital interfaces and experiences, to try to encourage, educate, coach, and remind people about how to better manage their condition.

Across the market we’re seeing the creation of digital interfaces and experiences that are tightly coupled to medications that aim to make them more personal, more accessible, and more convenient. These start from thinking about medications from the patient’s perspective, and asking the question: How can you simplify and strengthen these regimens in ways that makes them easier for people to understand and use?

JB:  The adherence failures you identified are an all-too-common cascade of risks for anyone prescribed a medication.  If the patient never fills a new prescription, we call it “primary non-adherence.”  This happens for 20-30% of new prescriptions written.  Of those who fill a prescription, about 50% don’t take it correctly, or stop prematurely.  Effective solutions need to understand why these failures occur, and prevent them from happening.  Our perspectives on this are informed by decades of research and our direct experience managing 8 million people’s medication adherence for health plans around the country.

Some of the reasons people don’t take medications are because they’ve decided, for what they think are good reasons, not to take the medication. This is actually most of the non-adherence that we observe in our work managing population-level adherence.  People either think that the medication didn’t work for them, that they experienced a side effect or fear a side effect that someone else told them about, or they made a choice not to pay for it because of its cost. Essentially, they considered the risks, the benefit, the convenience and the cost, and made a conscious decision not to take the medication.

Other patients want to be adherent but may be forgetful, have complex regimens they don’t understand, are inadvertently taking a drug incorrectly, can’t get access to the pharmacy or the product, or they want to take it but can’t afford it. That’s a different set of barriers.

Our approach is generally to predict who is at risk of these failures, determine the likely barriers, and then deliver an appropriate intervention that can overcome the barriers.

For patients who may choose not to adhere to their therapy, the answer is education and close follow-up, to make sure that they understand why they’re taking the medicine, how to take the medicine, what to expect from the medicine, so that they know it’s working or know it’s not working.  Close monitoring of lab values or clinical signs and symptoms can show them whether the medication is having the intended effect for them, and help them put that benefit in perspective relative to any side effects they may be feeling.  The goal here is to prevent non-adherence.

We published some work several years ago showing that in patients starting a cholesterol medicine, if you get them back into the doctor’s office within the first three months for a cholesterol test – their likelihood of being adherent over the subsequent year is far higher than if they don’t return for another lab test in that time period.  This notion of demonstrating the benefit of treatment early in therapy is really important because it balances against what they might perceive to be as the expense or the inconvenience or the side effects.  And it prevents non-adherence.

For the patient who is already receptive to therapy and wants to be adherent, we use interventions that address different barriers. This is where things like reminders, pillboxes, special unit-dose packaging, financial assistance programs, and home delivery can be helpful.  We’re trying to make it possible for the patient to be adherent to the regimen.

CH: I think when it comes to the refill issue and even first-fill challenges; the thing we really looked at is cost.

Our data indicates about a third of abandonment issues are usually due to cost concerns for an individual patient. A lot of that has come down to transparency. Any technology that can help bring that transparency, not only to the patient but also to the provider at the point of care, is going to be critical in creating a successful engagement and encouraging the patient to fill that medication and continue to take it.

Sadly, in many cases, there are alternatives that are less expensive that would still be clinically effective for the patient. But the provider and the patient are just unaware of what’s covered, what it would cost, and any programs that may enable the patient to get that medication at a lower cost. And so, I think that addresses that third point. In terms of the first point, what we look at is the friction from the point of prescribing to the pharmacy, to that fulfillment.

Today, there’s a huge amount of friction that really needs to be removed from the process, leading to abandonment and poor adherence. Many times, there’s a prior authorization on a drug and there is another drug that doesn’t have a prior authorization, so the patient gets to the pharmacy, can’t get it filled, and has to go through the prior authorization process, which can take days or weeks to complete.

We think that situation leads to poor adherence and has to be solved. Technology that can not only support that process and speed it up for the patient but ultimately solve it at the point of care, dealing with the PA immediately and not burdening the provider and the patient afterwards will be critical to the success of increasing that patient’s adherence. I also think about site of fulfillment, which can make a huge difference if that pharmacy a patient’s using is in network or out-of-network. Where is it on their plan?

Many times, the patient’s unaware or their provider is unaware of that. And so, technologies that can bring that information forward and help guide that patient to the right site of fulfillment are critical. It can be an actual physical pharmacy or even mail order. Anything from a technology standpoint that can address those issues is really going to have a massive impact on adherence.

To learn more about medication adherence, check out part 2 and part 3 of this medication adherence interview.

About David Van Sickle

David Van Sickle is co-founder and CEO of Propeller Health – the leader in respiratory digital health. David received his PhD in medical anthropology. His dissertation research, funded by the National Science Foundation, examined the rising prevalence of asthma and allergy in India. He was then an Epidemic Intelligence Service officer at the Centers for Disease Control and Prevention in Atlanta, where he was assigned to the Air Pollution and Respiratory Health Branch. During this time, he provided epidemiological support to the National Asthma Control Program, and investigated the health effects of a variety of environmental exposures. In addition, he helped establish emergency illness and injury surveillance in coastal Mississippi after Hurricane Katrina. David was also named a Champion of Change by the White House for his work on innovation.

About Josh Benner

A leading voice on medication adherence, Dr. Benner’s award-winning research and numerous publications have shed new light on the problem of nonadherence and identified promising approaches to improving it.  He is the founder and CEO of RxAnte, the leading provider of predictive analytics and targeted clinical programs for improving medication use.

Before joining RxAnte, Dr. Benner was Fellow and Managing Director at the Brookings Institution’s Center for Health Care Reform, where he focused on medical technology policy.

Prior to Brookings, Dr. Benner was principal at ValueMedics Research, an analytic and consulting services firm. Following the acquisition of ValueMedics by IMS Health in 2007, he served as senior principal in health economics and outcomes research and global lead for medication adherence at IMS. Dr. Benner received his Doctor of Pharmacy degree from Drake University and his Doctor of Science in health policy and management from the Harvard University School of Public Health.

About Carm Huntress

Carm Huntress is an entrepreneur and strategic leader with over 20 years of experience in startups focused around consumer and enterprise technology. His first web development and hosting company he started while in high school was eventually acquired in 2001.  After finishing his degree in electrical engineering at Northeastern University in 2004, he went on to work for PlumVoice, an IVR and voice technology startup, where he ran their network operations.  He later was asked to run product development at My Perfect Gig, a Northbridge and Commonwealth Venture start-up.

After two years as CTO at Reef Partners, where he ran the technology for a number of portfolio companies, he became CTO at Audiogon.com, the largest high end audio site in the world.  He managed the transition of the core technology platform and team for growth.  In 2013 he moved to Denver where he founded RxREVU.

A Model For Fostering Health Data Sharing

Posted on August 8, 2017 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.

Sometimes, I’m amazed by what Facebook’s advertising algorithm can do. While most folks get pitches for hot consumer devices, shoes or casual wear, I get pitched on some cool geek stuff.

Most recently, I got an interesting pitch from data.world, a social networking site that helps members share and discover open datasets. The site is free to join, and if there’s a paid “premium” setting I haven’t found it. From what I’ve seen, this is a pretty nifty model which could easily be adapted for use by health IT organizations.

The site, which looks and feels something like Facebook, features data from a wide range of industries, tilted heavily toward government databases. For example, when I checked in, a front-page column listing the most commonly used tags includes “GIS,” “Homeland Security,” “police,” “SBA” and “DC” (which lead the pack with 688 mentions).

And there’s plenty of healthcare industry data to grab if you’d like. If you search for the term “healthcare” some useful datasets pop up, including a list of last year’s hospital HCAHPS ratings, California-specific data from 2005 to 2014 on the number and rates of preventable hospitalizations for selected medical conditions and New York state data on payments it made under its Medicaid Electronic Health Record Incentive Program. (You’ll have to become a site member to access these records.)

What makes the site truly interesting is the data sharing mechanism it offers. As a member, you have a chance to both upload open datasets, download datasets, post a project or join someone else’s project already in progress. Want help crunching the data on preventable hospitalizations in California? Let other site members know. There’s at least a chance you’ll find great project partners.

Of course, I’m not here to shill for this particular venture. My point in writing about its features is to draw your attention to what it does.

I think it’s more than time for healthcare organizations to collaborate on shared data projects together, and this is perhaps one mechanism for doing so. True, most of the data health systems work with is proprietary, but perhaps it’s possible to work past this issue.

Some healthcare organizations have already decided that sharing otherwise proprietary data is worth the risk. For example, late last year I wrote about a project undertaken by Sioux Falls, SD-based Sanford Health, in which the health system shared clinical data with a handful of academic researchers.  Benson Hsu, MD, vice president of enterprise data and analytics for the system, told Healthcare IT News this “crowdsourced” approached helped Sanford predict risk more effectively and improved its chronic disease management efforts.

Admittedly, Sanford’s approach won’t work for everyone. Today, healthcare organizations aren’t in the habit of cooperating on clinical data analytics projects, and anyone who suggests the idea is likely to get some serious pushback. Yes, in theory we all want interoperability, but this is different. Sharing entire clinical data repositories is a big deal. Still, how are we going to tackle big problems like population health management if we aren’t open to data analytics collaboration?

Sometimes new initiatives happen because people learn to understand each other’s needs, and decide that the prospect of mutual gain is worth the risk. I think a community devoted to data analytics could do much to foster such relationships.

Embracing Quality: What’s Next in the Shift to Value-Based Care, and How to Prepare

Posted on June 13, 2017 I Written By

The following is a guest blog post by Brad Hill, Chief Revenue Officer at RemitDATA.

Whatever the future holds for the Affordable Care Act (ACA), the shift to value-based care is likely here to stay. The number of providers and payers implementing value-based reimbursement contracts has grown steadily over the past few years. A survey of 465 payers and hospitals conducted in 2016 by ORC International and McKesson revealed that 58 percent are moving forward with incorporating value-based reimbursement protocols. The study, “Journey to Value: The State of Value-Based Reimbursements in 2016” further revealed that as healthcare continues to adopt full value-based reimbursement, bundled payments are the fastest growing with projections that they will continue to grow the fastest over the next five years, and that network strategies are changing, becoming narrower and more selective, creating challenges among many payers and hospitals as they struggle to scale these complex strategies.

Given the growth of adoption of value-based care, there are certainly many hurdles to clear in the near future as policymakers decide on how they plan to repeal and replace the ACA. A January 2017 report by the Urban Institute funded by the Robert Wood Johnson Foundation revealed that some of the top concerns with some potential scenarios being floated by policymakers include concerns over an immediate repeal of the individual mandate with delayed repeal of financial subsidies; delayed repeal of the ACA without its concurrent replacement; and a cutoff of cost-sharing subsidies in 2017.

With the assumption that value-based healthcare is here to stay, what steps can you take to continue to prepare for value-based payments? The best advice would be to continue on with a “business as usual” mindset, stay focused and ensure all business processes are ready for this shift by continuing to:

  1. Help providers establish baselines and understand their true cost of conducting business as a baseline for assuming risk.
  2. Analyze your revenue cycle. Look at the big picture for your practice to analyze service costs and reimbursements for each – determine if margins are in-line with peers.  Identify internal staff processing time and turnaround times by payer. Evaluate whether there are any glaring issues or problems that need to be addressed to reduce A/R days and improve reimbursement rates.
  3. Determine whether there are reimbursement issues for specific payers or if the problem is broader in nature. Are your peers experiencing the same issues with the same payers?
  4. Capture data analysis for practice improvement. With emerging payment models, hospitals and practices will need expertise in evaluating data and knowledge in how to make business adjustments to keep the organization profitable.
  5. Determine how you can scale and grow specific payment models. Consider, for example, a provider group that maintains 4 different payment models and 10 different payers. The provider group will need to determine whether this system is sustainable once payment models shift.
  6. Break down department silos in determining cost allocation rules. Providers need a cost accounting system that can help determine exact costs needed to provide care and to identify highest cost areas. Cost accounting systems are typically managed by the finance team. There needs to be clinical and operational input from all departments to make a difference. Collaborate across all departments to determine costs, and design rules and methodologies that take each into account.
  7. Compare your financial health to that of your peers. Comparative analytics can help by giving you insights and data to determine your practice’s operational health. Determine whether you are taking longer to submit claims than your peers, have a higher percentage of denied claims for a specific service, percentage of billed to allowed amounts and more.

Though change is a part of the healthcare industry’s DNA, ensuring business processes are in line, and leveraging data to do so will help organizations adapt to anything that comes their way.

The EMR Vendor’s Dilemma

Posted on June 6, 2017 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.

Yesterday, I had a great conversation with an executive at one of the leading EMR vendors. During our conversation, she stressed that her company was focused on the future – not on shoring up its existing infrastructure, but rather, rebuilding its code into something “transformational.”

In describing her company’s next steps, she touched on many familiar bases, including population health, patient registries and mobile- first deployment to support clinicians. She told me that after several years of development, she felt her company was truly ready to take on operational challenges like delivering value-based care and conducting disease surveillance.

All that being said – with all due respect to the gracious exec with whom I spoke – I wouldn’t want to be a vendor trying to be transformed at the moment. As I see it, vendors who want to keep up with current EMR trends are stuck between a rock and a hard place.

On the one hand, such vendors need to support providers’ evolving health IT needs, which are changing rapidly as new models of care delivery are emerging. Not only do they need to provide the powerhouse infrastructure necessary to handle and route massive floods of data, they also need to help their customers reach and engage consumers in new ways.

To do so, however, they need to shoot at moving targets, or they won’t meet provider demand. Providers may not be sure what shape certain processes will take, but they still expect EMR vendors to keep up with their needs nonetheless. And that can certainly be tricky these days.

For example, while everybody is talking about population health management, as far as I know we still haven’t adopted a widely-accepted model for adopting it. Sure, people are arriving at many of the same conclusions about pop health, but their approach to rolling it out varies widely.  And that makes things very tough for vendors to create pop health technology.

And what about patient engagement solutions? At present, the tools providers use to engage patients with their care are all over the map, from portals to mobile apps to back-end systems using predictive analytics. Synchronizing and storing the data generated by these solutions is challenging enough. Figuring out what configuration of options actually produces results is even harder, and nobody, including the savviest EMR vendors, can be sure what the consensus model will be in the future.

Look, I’m aware that virtually all software vendors face this problem. It’s difficult as heck to decide when to lead the industry you serve and when to let the industry lead you. Straddling these two approaches successfully is what separates the men from the boys — or the girls from the women — and dictates who the winners and losers are in any technology market.

But arguably, health IT vendors face a particularly difficult challenge when it comes to keeping up with the times. There’s certainly few industries are in a greater state of flux, and that’s not likely to change anytime soon.

It will take some very fancy footwork to dance gracefully with providers. Within a few years, we’ll look back and know vendors adapted just enough.

Few Practices Rely Solely On EMR Analytics Tools To Wrangle Data

Posted on May 23, 2017 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 done by a trade group representing medical practices has concluded that only a minority of practices are getting full use of their EMR’s analytics tools.

The survey, which was reported on by Becker’s Hospital Review, was conducted by the Medical Group Management Association.  The MGMA’s survey called on about 900 of its members to ask how their practices used EMRs for analytics.

First, and most unexpectedly in today’s data-driven world, 11 percent of respondents said that they don’t analyze their EMR data at all.

Thirty-one percent of respondents told MGMA that they use all of their EMR’s analytical capabilities, and 22 percent of respondents said they used some of their EMR’s analytics capabilities.

Another 31 percent reported that they were using both their EMR’s analytics tools and tools from an external vendor. Meanwhile, 5 percent said they used only an external vendor for data analytics.

According to Derek Kosiorek, CPEHR, CPHIT, principal consultant with MGMA’s Health Care Consulting Group, the survey results aren’t as surprising as they may seem. In fact, few groups are likely to get  everything they need from EMR data, he notes.

“Many practices do not have the resources to mine the data and organize it in ways to create new insights from the clinical, administrative and financial information being captured daily,” said Kosiorek in a related blog post. “Even if your practice has the staff with the knowledge and time to create reports, the system often requires an add-on product sold by the vendor or an outside product or service to analyze the data.”

However, he predicts that this will change in the near future. Not only will EMR analytics help groups to tame their internal data, it will also aggregate data from varied community settings such as the emergency department, outpatient care and nursing homes, he suggests. He also expects to see analytics tools offer a perspective on care issues brought by regional data for similar patients.

At this point I’m going to jump in and pick up the mic. While I haven’t seen anyone from MGMA comment on this, I think this data – and Kosiorek’s comments in particular – underscore the tension between population health models and day-to-day medical practice. Specifically, they remind us that doctors and regional health systems naturally have different perspectives on why and how they use data.

On the one hand there’s medical practices which, from what I’ve seen, are of necessity practical. These providers want first and foremost to make individual patients feel good and if sick get better. If that can be done safely and effectively I doubt most care about how they do it. Sure, doctors are aware of pop health issues, but those aren’t and can’t be their priority in most cases.

Then, you have hospitals, health systems and ACOs, which are already at the forefront of population health management. For them, having a consistent and comprehensive set of tools for analyzing clinical data across their network is becoming job one. That’s far removed from focusing on day-to-day patient care.

It’s all well and good to measure whether physicians use EMR analytics tools or not. The real issue is whether large health organizations and practices can develop compatible analytics goals.

The Sexiest Data in Health IT: Datapalooza 2017

Posted on May 15, 2017 I Written By

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. twitter: @coherencemed

The data at this conference was the Best Data. The Biggest Data. No one has better data than this conference.

The sexiest data in all of healthIT was highlighted in Washington DC at Datapalooza April 27-28, 2017.  One of the main themes was how to deal with social determinants of health and the value of that data.  Sachin H. Jain, MD of Caremore Health reminded us that “If a patient doesn’t have food at home waiting for them they won’t get better” social data needs to be in the equation. Some of the chatter on the subject of healthcare reform has been criticism that providing mandatory coverage hasn’t always been paired with knowledge of the area. If a patient qualifies for Medicaid and has a lower paying job how can they afford to miss work and get care for their health issues?
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Rural areas also have access issues. Patient “Charles” works full time during the week and qualifies for Medicaid. He can’t afford to miss a lot of work but needs a half a day to get treatments which affect his ability to work. There is no public transportation in his town to the hospital in a city an hour and a half away. Charles can’t afford the gas or unpaid time off work for his treatment.

Urban patient “Haley” returns to her local ER department more than once a week with Asthma attacks.  Her treatments are failing because she lives in an apartment with mold in the walls. As Craig Kartchner from the Intermountain Healthcare team responded to the #datapalooza  hashtag online- These can be the most difficult things to change.

The 2016 report to Congress addresses the difficulty of the intersection between social factors and providing quality healthcare in terms of Social Determinants of Health:

“If beneficiaries with social risk factors have worse health outcomes because the providers they see provide low quality care, value based purchasing could be a powerful tool to drive improvements in care and reduce health disparities. However, if beneficiaries with social risk factors have worse health outcomes because of elements beyond the quality of care provided, such as the social risk factors themselves, value based payment models could do just the opposite. If providers have limited ability to influence health outcomes for beneficiaries with social risk factors, they may become reluctant to care for beneficiaries with social risk factors, out of fear of incurring penalties due to factors they have limited ability to influence.”

Innovaccer just launched a free tool to help care teams track and monitor Medicare advantage plans. I went to their website and looked at my county and found data about the strengths in Salt Lake where I’m located. They included:

  • Low prevalence of smoking
  • Low Unemployed Percentage
  • Low prevalence of physically inactive adults

Challenges for my area?

  • Low graduation rate
  • High average of daily Air pollution
  • High income inequality
  • High Violent crime rate per 100,000 population

Salt Lake actually has some really bad inversion problems during the winter months and some days the particulate matter in the air creates problems for respiratory problems. During the 2016-2017 winter there were 18 days of red air quality and 28 days of yellow air quality. A smart solution for addressing social determinants of health that negatively impact patients in this area could be addressing decreasing air pollution through increased public transportation. Healthcare systems will see an increase in cost of care during those times and long term population health challenges can emerge. You can look at your county after you enter your email address on their site. This kind of social data visualization can give high level insights into the social factors your population faces.

One of the themes of HealthDataPalooza was how to use system change to navigate the intersection between taking care of patients and not finding way to exclude groups. During his panel discussion of predictive analytics, Craig Monson the medical director for analytics and reporting discussed how “data analytics is the shiny new toy of healthcare.”    In addition to winning the unofficial datapalooza award for the most quotes and one liners – Craig presented the Clinical Risk Prediction Initiative (CRISPI).  This is a multi variable logistic regression model with data from the Atrius health data warehouse. His questions for systems to remember in their data analysis selection are “Who is the population you are serving? What is the outcome you need? What is the intervention you should implement?”

Warning- Craig reminds us that in a world of increasing sexy artificial intelligence coding a lot of the value analysis can be done with regression. Based on that statement alone I think he can be trusted. I still need to see his data.

CRISPI analyzed the relative utility of certain types of data, and didn’t have a large jump in utility when adding Social Determinant Data. This data was one of the most popular data sets during Datapalooza discussions but the reality of making actionable insights into system improvement? Craig’s analysis said it was lacking. Does this mean social determinant data isn’t significant or that it needs to be handled with a combination of traditional modeling and other methods?  Craig’s assertion seemed to fly in the face of the hot new trend of Social Determinants of Health data from the surface.

Do we have too much data or the wrong use of the data? Most of the companies investing into this space used data sources outside the traditional definition to help create solutions with social determinate of health and Patient outcomes. They differed in how they analyzed social determinant data. Traditional data sources for the social determinants of health are well defined within the public health research.  The conditions in which you work and live impact your health.

Datapalooza had some of the greatest minds in data analytics and speakers addressed gaps in data usefulness. Knowing that a certain large county wide population has a problem with air quality might not be enough to improve patient outcomes. There is need for analysis of traditional data sources in this realm and how they can get meaningful impact for patients and communities. Healthcare innovators need to look at different data sources.  Nick Dawson, Executive director of Johns-Hopkins Sibley Innovation Hub responded to the conversation about food at home with the data about Washington DC.  “DC like many cities has open public data on food scarcity. But it’s not part of a clinical record. The two datasets never touch.” Data about food scarcity can help hospital systems collaborate with SNAP and Government as well as local food programs. Dawson leads an innovation lab at Johns Hopkins Sibley where managers, directors, VPs and C Suite leaders are responsible for working with 4 innovation projects each year.

Audun Utengen, the Co Founder of Symplur said “There’s so much gold in the social media data if you choose to see it.” Social data available online helps providers meet patients where they are and collect valuable data.  Social media data is another source to collect data about patient preferences and interactions for reaching healthcare populations providers are trying to serve. With so much data available sorting through relevant and helpful data provides a new challenge for healthcare systems and providers.

New Data sources can be paired with a consultative model for improving the intersection of accountable care and lack of access due to social factors. We have more sophisticated analytic tools than ever for providing high value care in the intersection between provider responsibility and social collaboration. This proactive collaboration needs to occur on local and national levels.  “It’s the social determinants of health and the behavioral aspects that we need to fund and will change healthcare” we were reminded. Finding local community programs that have success and helping develop a strategy for approaching Social Determinants of Health is on the mind of healthIT professionals.

A number of companies examine data from sources such as social media and internet usage or behavioral data to design improvements for social determinants of health outcomes.   They seek to bridge the gaps mentioned by Dawson. Data sets exist that could help build programs for social determinants of health.  Mandi Bishop started Lifely Insights centered around building custom community plans with behavioral insights into social determinant data. Health in all Policies is a government initiative supporting increased structure and guidelines in these areas. They support local and State initiatives with a focus on prevention.

I’m looking forward to seeing how the data landscape evolves this year. Government Challenges such as the Healthy Behavior Data Challenge launched at Datapalooza will help fund great improvements. All the data people will get together and determine meaningful data sets for building programs addressing the social determinants of health. They will have visualization tools with Tableau. They will find ways to get food to patients at home so those patients will get better. Programs will find a way to get care to rural patients with financial difficulty and build safe housing.

From a healthcare delivery perspective the idea of collaborating about data models can help improve community health and decrease provider and payer cost. The social determinants of health can cost healthcare organizations more money than data modeling and proactive community collaboration.

Great regressions, saving money and improving outcomes?

That is Datapalooza.