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Let Vendors Lead The Way? Are You Nuts?

Posted on August 13, 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.

Every now and then, a vendor pops up and explains how the next-gen EHR should work. It’s easy to ask yourself why anyone should listen, given that you’re the one dishing out the care. But bear with me. I’ve got a theory working here.

First of all, let’s start with a basic assumption, that EHRs aren’t going to stay in their current form much longer. We’re seeing them grow to encompass virtually every form of medical data and just about every transaction, and nobody’s sure where this crazy process is going to end.

Who’s going to be our guide to this world? Vendors. Yup, the people who want to sell you stuff. I will go out on a limb and suggest that at this point in the health data revolution, they’re in a better position to predict the future.

Sure, that probably sounds obnoxious. While vendors may employ reputable, well-intended physicians, the vast majority of those physicians don’t provide care themselves anymore. They’re rusty. And unless they’re in charge of the company they serve, their recommendations may be overruled by people who have never touched a patient.

On the flip side, though, vendor teams have the time and money to explore emerging technologies, not just the hip stuff but the ones that will almost certainly be part of medical practice in the future. The reality is that few practicing physicians have time to keep up with their progress. Heck, I spend all day researching these things, and I’m going nuts trying to figure out which tech has gone from a nifty idea to a practical one.

Given that vendors have the research in hand, it may actually make sense to let them drive the car for a while. Honestly, they’re doing a decent job of riding the waves.

In fact, it seems to me that the current generation of health data management systems are coming closer to where they should be.  For example, far more of what I’d call “enhanced EHR” systems include care management tools, integrating support for virtual visits and modules that help practices pull together MIPS data. As always, they aren’t perfect – for example, few ambulatory EHRs are flexible enough to add new functions easily — but they’re getting better.

I guess what I’m saying is that even if you have no intention of investing in a given product, you might want to see where developers’ ideas are headed. Health data platforms are at an especially fluid stage right now, tossing blockchain, big data analytics, AI and genomic data together and creating new things. Let’s give developers a bit of slack and see what they can do to tame these beasts.

How Are Ambulatory Practices Going to Compete with Health Systems?

Posted on July 9, 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.

We’ve all seen the stories about the explosion of data and the way healthcare is getting more personalized. However, David Chou recently pointed out how the data is one thing, but figuring out the role everyone plays in your healthcare organization is just as important as the data itself. It gets complex quickly as this graphic David shared shows:

This is a great graphic of the healthcare analytics roles and responsibilities that will be needed to make the personalized medicine future a reality. Plus, it will be key to getting a lot of the value out of our past EHR investments. Many hospitals and health systems already have these roles filled or are working to have them filled. We’ve seen this first hand when we see data jobs being posted to our healthcare IT job board.

While this work is extremely exciting and shows a lot of promise, I imagine a graphic like the one above is just completely overwhelming to consider for a small ambulatory practice. Even a large group practice would likely find the above graphic challenging to consider in their relatively small healthcare organization. How can they compete with a large health system with that kind of complexity? Do graphics like the one above just provide one other illustration of why small practices are going to soon be extinct?

I don’t think so and I hope not. However, graphics like the one above do illustrate the tremendous challenges that ambulatory practices face when they don’t have a massive health system behind them. What’s the path forward for smaller practices then?

The first thing to remember is that even though a health system is large, it doesn’t mean it’s going to do things well. In fact, it’s easy to argue how large organizations are much less efficient. It’s not hard to see how a large health system will focus all of their analytics work on the acute care environment and leaves out ambulatory practices. Smaller healthcare organizations are going to have to use this to their advantage.

While it’s unlikely that ambulatory practices will do all of the healthcare analytics work on their own, it is possible for ambulatory practices to tap into third party vendors that do the work for them and hundreds of other ambulatory practices. Smaller healthcare organizations partnering with corporate and entrepreneurial vendors is going to be the best way for these healthcare organizations to compete with the large health system. In fact, it’s a huge opportunity for them to show why patients should visit their practice instead of the large health system.

One thing that’s holding these efforts back is EHR vendors’ decision to close the doors to outside vendors. There are a few EHR vendor exceptions and areas where every EHR vendor is more open (ie. labs, pharmacy, etc), but it won’t be enough going forward. My friend Jeremy Coleman recently described why in this series of tweets:

I don’t see any healthcare future where centralization will survive. Sure, it will put up a good fight for a while, but the number and variety of applications that are coming out in healthcare are going to be so varied and dramatically important for doctors to incorporate into the care they provide that EHR vendors won’t have a choice but to create APIs that facilitate all of these applications.

An EHR vendor that embraces this approach is going to be essential for every ambulatory practice. Eventually, ambulatory practices will be stuck with the need to switch EHR systems or sell to the health system (which generally means switching EHR systems too). However, an ambulatory EHR that provides an open ecosystem for the latest and greatest in health IT will allow ambulatory practices to thrive even against the much larger health systems.

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.

Recording Doctor-Patient Visits Shows Great Potential

Posted on June 1, 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.

Doctors, do you know how you would feel if a patient recorded their visit with you? Would you choose to record them if you could? You may soon find out.

A new story appearing in STAT suggests that both patients and physicians are increasingly recording visits, with some doctors sharing the audio recording and encouraging patients to check it out at home.

The idea behind this practice is to help patients recall their physician’s instructions and adhere to treatment plans. According to one source, patients forget between 40% to 80% of physician instructions immediately after leaving the doctor’s office. Sharing such recordings could increase patient recall substantially.

What’s more, STAT notes, emerging AI technologies are pushing this trend further. Using speech recognition and machine learning tools, physicians can automatically transcribe recordings, then upload the transcription to their EMR.

Then, health IT professionals can analyze the texts using natural language processing to gain more knowledge about specific diseases. Such analytics are likely to be even more helpful than processes focused on physician notes, as voice recordings offer more nuance and context.

The growth of such recordings is being driven not only by patients and their doctors, but also by researchers interested in how to best leverage the content found in these recordings.

For example, a professor at Dartmouth is leading a project focused on creating an artificial intelligence-enabled system allowing for routine audio recording of conversations between doctors and patients. Paul Barr is a researcher and professor at the Dartmouth Institute for Health Policy and Clinical Practice.

The project, known as ORALS (Open Recording Automated Logging System), will develop and test an interoperable system to support routine recording of patient medical visits. The fundamental assumption behind this effort is that recording such content on smart phones is inappropriate, as if the patient loses their phone, their private healthcare information could be exposed.

To avoid this potential privacy breach, researchers are storing voice information on a secure central server allowing both patients and caregivers to control the information. The ORALS software offers both a recording and playback application designed for recording patient-physician visits.

Using the system, patients record visits on their phone, have them uploaded to a secure server and after that, have the recordings automatically removed from the phone. In addition, ORALS also offers a web application allowing patients to view, annotate and organize their recordings.

As I see it, this is a natural outgrowth of the trailblazing Open Notes project, which was perhaps the first organization encouraging doctors to share patient information. What makes this different is that we now have the technology to make better use of what we learn. I think this is exciting.

Competition Heating Up For AI-Based Disease Management Players

Posted on May 21, 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.

Working in collaboration with a company offering personal electrocardiograms to consumers, researchers with the Mayo Clinic have developed a technology that detects a dangerous heart arrhythmia. In so doing, the two are joining the race to improve disease management using AI technology, a contest which should pay the winner off handsomely.

At the recent Heart Rhythm Scientific Sessions conference, Mayo and vendor AliveCor shared research showing that by augmenting AI with deep neural networks, they can successfully identify patients with congenital Long QT Syndrome even if their ECG is normal. The results were accomplished by applying AI from lead one of a 12-lead ECG.

While Mayo needs no introduction, AliveCor might. While it started out selling a heart rhythm product available to consumers, AliveCor describes itself as an AI company. Its products include KardiaMobile and KardiaBand, which are designed to detect atrial fibrillation and normal sinus rhythms on the spot.

In their statement, the partners noted that as many as 50% of patients with genetically-confirmed LQTS have a normal QT interval on standard ECG. It’s important to recognize underlying LQTS, as such patients are at increased risk of arrhythmias and sudden cardiac death. They also note that that the inherited form affects 160,000 people in the US and causes 3,000 to 4,000 sudden deaths in children and young adults every year. So obviously, if this technology works as promised, it could be a big deal.

Aside from its medical value, what’s interesting about this announcement is that Mayo and AliveCor’s efforts seem to be part of a growing trend. For example, the FDA recently approved a product known as IDx-DR, the first AI technology capable of independently detecting diabetic retinopathy. The software can make basic recommendations without any physician involvement, which sounds pretty neat.

Before approving the software, the FDA reviewed data from parent company IDx, which performed a clinical study of 900 patients with diabetes across 10 primary care sites. The software accurately identified the presence of diabetic retinopathy 87.4% of the time and correctly identified those without the disease 89.5% of the time. I imagine an experienced ophthalmologist could beat that performance, but even virtuosos can’t get much higher than 90%.

And I shouldn’t forget the 1,000-ton presence of Google, which according to analyst firm CBInsights is making big bets that the future of healthcare will be structured data and AI. Among other things, Google is focusing on disease detection, including projects targeting diabetes, Parkinson’s disease and heart disease, among other conditions. (The research firm notes that Google has actually started a limited commercial rollout of its diabetes management program.)

I don’t know about you, but I find this stuff fascinating. Still, the AI future is still fuzzy. Clearly, it may do some great things for healthcare, but even Google is still the experimental stage. Don’t worry, though. If you’re following AI developments in healthcare you’ll have something new to read every day.

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.

#HIMSS18 First Day:  A Haze Of Uncertainty

Posted on March 7, 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.

Entering the HIMSS exhibit area always feels like walking straight into a hurricane. But if you know how to navigate the show, things usually start to come into focus.

There’s a bunch of young, scrappy and hungry startups clustered in a hive, a second tier of more-established but still emerging ventures and a scattering of non-healthcare contenders hoping to crack the market. And of course, there are the dream places put in place by usual suspects like Accenture, SAP and Citrix. (I also stumbled across a large data analytics company, the curiously-named splunk> — I kid you not – whose pillars of data-like moving color squares might have been the most spectacular display on the floor.)

The point I’m trying to make here is that as immense and overwhelming as a show like HIMSS can be, there’s a certain order amongst the chaos. And I usually leave with an idea of which technologies are on the ascendance, and which seem the closest to practical deployment. This time, not so much.

I may have missed something, but my sense on first glance that I was surrounded by solutions that were immature, off-target or backed by companies trying to be all things to all people. Also, surprisingly few even spoke the word “doctor” when describing their product.

For example, a smallish HIT company probably can’t address IoT, population health, social determinants data and care coordination in one swell foop, but I ran into more than one that was trying to do something like this.

All told, I came away with a feeling that many vendors are trapped in a haze of uncertainty right now. To be fair, I understand why. Most are trying to build solutions without knowing the answers to some important questions.

What are the best uses of blockchain, if any? What role should AI play in data analytics, care management and patient interaction? How do we best define population health management? How should much-needed care coordination technologies be architected, and how will they fit into physician workflow?

Yes, I know that vendors’ job is to sort these things like these out and solve the problems effectively. But this year, many seem to be struggling far more than usual.

Meanwhile, I should note that there seems to be a mismatch between what vendors showed up and what providers say that they want. Why so few vendors focused on RCM or cybersecurity, for example? I know that to some extent, HIMSS is about emerging tech rather than existing solutions, but the gap between practical and emerging solutions seemed larger than usual.

Don’t get me wrong – I’m learning a lot here. The wonderful buzz of excited conversations in the hall is as intense as always. And the show is epic and entertaining as always. Let’s hope that next year, the fog has cleared.

Partners AI System Gives Clinicians Better Information

Posted on January 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.

While HIT professionals typically understand AI technology, clinicians may not. After all, using AI usually isn’t part of their job, so they can be forgiven for ignoring all of the noise and hype around it.

Aware of this problem, Partners Connected Health and partner Hitachi have come together to create an AI-driven process which isolates data physicians can use. The new approach, dubbed ‘explainable AI,’ is designed to list the key factors the system has relied upon in making projections, making it easier for physicians to make relevant care decisions.

Explainable AI, a newer term used by the two organizations, refers not only to the work being done to develop the Partners system, but also a broader universe in which machines can explain their decisions and actions to human users. Ultimately, explainable AI should help users trust and use AI tools effectively, according to a Hitachi statement.

Initially, Partners will use the AI system to predict the risk of 30-day readmissions for patients with heart failure. Preventing such readmissions can potentially save $7,000 per patient per year.

The problem is, how can organizations like Partners make AI results useful to physicians? Most AI-driven results are something of a black box for clinicians, as they don’t know what data contributed to the score. After all, the algorithm analyses about 3,000 variables that might be a factor in readmissions, drawing from both structured and unstructured data. Without help, there’s little chance physicians can isolate ways to improve their own performance.

But in this case, the AI system offers much better information. Having calculated the predictive score, it isolates factors that physicians can address directly as part of the course of care. It also identifies which patients would be the best candidates for a post-discharge program focused on preventing readmissions.

All of this is well and good, but will it actually deliver the results that Partners hoped for? As it turns out, the initial results of a pilot program are promising.

To conduct the pilot, the Partners Connected Health Innovation team drew on real-life data from heart failure patients under its care. The patients were part of the Partners Connected Cardiac Care Program, a remote monitoring education program focused on managing their care effectively in reducing the risk of hospitalization.

The test compared the results calculated by the AI system with real-life results drawn from about 12,000 heart failure patients hospitalized and discharged from the Partners HealthCare network in 2014 in 2015. As it turned out, there was a high correlation between actual patient readmissions and the level predicted by the system. Next, Partners will share a list of variables that played the biggest role in the AI’s projects. It’s definitely a move in the right direction.