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Healthcare AI Adoption Curve – Where Is Your Hospital At?

Posted on July 30, 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.


The above image is the best one I’ve seen when it comes to the adoption and integration of AI into healthcare. Of course, this same chart has been used to describe the integration of technology into healthcare in general. The reason this chart is so relevant is that very few healthcare organizations have reached the point where they are an IT enabled business with IT embedded in business with hybrid, cross-functional roles. If this is true for technology in general, AI is still way out there.

In fact, the one complaint I have about this chart is that it’s missing a bubble that should say “What’s AI?” Ok, that’s a little bit of an exaggeration, but not much for many healthcare organizations. They’d more appropriately ask “How can I use AI in healthcare?” but it’s about the same point. Most aren’t there yet, but they’re going to have to get there. AI is coming and in a big way.

The good news is that most of the AI a healthcare organization will use will be embedded in the IT systems they purchase. This is why it’s so important that healthcare organizations have good vendor partners. Healthcare organizations aren’t going to enable this AI future. They’re going to partner with vendors who bring the AI to bear for them. When David Chou shared the image above, he asked the right question “What is your role as the CIO for the adoption of AI?” How many of you know the answer to that question?

If you’re not sure the answer, check out this other image and tweet that David Chou shared about using AI for automation:

I agree 100% with David Chou that if you want to start thinking about how to utilize AI, then start with repetitive tasks which can and should be automated. Take the mundane out of your healthcare providers lives. That will create some early AI wins that will help you to be able to build an AI driven culture in your organization.

Clinicians Say They Need Specialized IT To Improve Patient Safety

Posted on July 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Hospitals are loaded down with the latest in health IT and have the bills to prove it. But according to a new survey, they need to invest in specialized technologies to meet patient safety goals, as well as providing more resources and greater organizational focus.

Health Catalyst recently conducted a national survey of physicians, nurses and health executives to gather their thoughts on patient safety issues. Among its main findings was that almost 90% of respondents said that their organizations were seeing success in improving patient safety. However, about the same percentage said there was room for improving patient safety in their organization.

The top obstacle they cited as holding them back from the patient safety goals was having effective information technology, as identified by 30% of respondents. The same number named a lack of technologies offering real-time warnings of possible patient harm.

These were followed by lack of staffing and budget resources (27%), organizational structure, culture priorities (19%), a lack of reimbursement for safety initiatives (10%) and changes in patient population practice setting (9%).

Part of the reason clinicians aren’t getting as much as they’d like from health IT is that many healthcare organizations rely largely on manual methods to track and report safety events.

The top sources of data for patient safety initiatives respondents used for safety initiatives voluntary reporting (82%). Hospital-acquired infection surveys (67%), manual audits (58%) and retrospective coding (29%). Such reporting is typically based on data sets which are at least 30 days old, and what’s more, collecting and analyzing the data can be time and resource-consuming.

Not surprisingly, Health Catalyst is launching new technology designed to address these problems. Its Patient Safety Monitor™ Suite: Surveillance Module uses protective and text analytics, along with concurrent critical reviews of data, to find and prevent patient safety threats before they result in harm.

The announcement also falls in line with the organization’s larger strategic plans, as Health Catalyst has applied to the AHRQ to be certified as a Patient Safety Organization.

The company said that he had spent more than $50 million to create the Surveillance module, whose technology includes the use of predictive analytics models and AI. It expects to add new AI and machine learning capabilities to its technology in the future which will be used to propose strategies to eliminate patient safety risks.

And more is on the way. Health Catalyst is working with its clients to add new features to the Suite including risk prediction, improvement tracking and decision support.

I’m not sure if it’s typical for PSOs to bringing their own specialized software to the job, but either way, it should give Health Catalyst a leg up. I have little doubt that doing better predictive analytics and offering process recommendations would be useful.

Rate Of Healthcare Ransomware Attacks Falls In First Half of 2018

Posted on July 12, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Most research I’ve read lately suggests that the rate of healthcare cyberattacks is at an all-time high, and that ransomware is leading the parade.

But is that really true? Maybe not. A new security report has concluded that the rate of ransomware attacks on healthcare organizations actually fell during the first half of this year, and what’s more, that such attacks trended lower during the same period.

The study, which comes from security firm CryptoniteNXT, notes that cybercriminals target healthcare because they can fetch great prices for the data by reselling it on the dark web. Also, given the complexity of healthcare networks and the high number of vulnerabilities in those networks, thieves see providers as a fat and easy target.

However, when it comes to ransomware, the landscape may be changing. CryptoniteNXT found that the number of ransomware attacks impacting over 500 patient records dropped from 19 major data breaches in the first half of 2017 to 8 major breaches in the first half of 2018. That’s an impressive 57% decrease.

The biggest reported records IT/hacker-driven breach hit LifeBridge Health, affecting 538,127 individuals. Other organizations targeted included academic medical centers, medical practices, ambulatory surgical centers, health plans and government agencies.

Meanwhile, the rate of ransomware attacks as a percentage of IT/hacking events has fallen substantially, from 30.16% during the first half of 2017 to 13.6% during the first half of this year.

On the other hand, the volume of patients affected has climbed. Roughly 1.9 million patient records were breached in the first half of this year, compared with 1.7 million records the first half of 2017 and 1.8 million records the second half of that year, it concludes.

Also, the report notes that ransomware attackers are far from done with the industry. The authors say that ransomware will still pose a “formidable threat” to healthcare organizations and that new variants such as AI-based malware will pose a major threat to healthcare organizations for the next couple of years.

To fend off hacking attacks, CryptoniteNXT recommends adopting new best practices such as moving target cyber defense and network micro-segmentation, which can address the inherent weakness of TCP/IP networks.

3 Key Steps to Driving your Revenue Strategy

Posted on July 9, 2018 I Written By

The following is a guest blog post by Brad Josephson is the Director of Marketing and Communications at PMMC.

For healthcare providers struggling to accurately collect reimbursement, developing a revenue strategy based off a foundation of accuracy is the most efficient way to ensure revenue integrity throughout the revenue cycle.

Currently, many hospitals operate under multiple systems running for their different departments within the organization. This type of internal structure can threaten the accuracy of the analytics because data is forced to come into multiple systems, increasing the chances that the data will be misrepresented.

By maintaining revenue integrity, not only does it give hospitals assurance that the data they’ve collected is current and accurate, but it also provides invaluable leverage with the payer when it comes time to (re)negotiating payer contracts.

Let’s begin by starting from the ground up…

Here are the 3 steps needed for maintaining revenue integrity:

  • Creating a foundation backed by accurate analytics
  • Breaking down the departmental siloes
  • Preparing ahead of time for consumerism and price transparency

Accuracy Drives Meaningful Analytics

The first step toward maintaining revenue integrity is to assess whether your data is accurate. We know that accurate data drives meaningful analytics, essentially functioning as the engine of the revenue cycle.

And what happens when you stop taking care of the engine regularly and it no longer works properly? It not only costs you a lot of money to repair the engine, but you may also have to pay for other parts of the car that were damaged by the engine failure.

What if, however, you were able to visualize pie charts and bar graphs on your car’s dashboard that showed the current health of the engine to inform you when it requires a maintenance check?

You would be better informed about the current state of your engine and have a greater urgency to get the car repaired.

This same principle applies to healthcare organizations looking to increase the accuracy of their data to drive meaningful analytics. While some organizations struggle to draw valuable insight from pieces of raw data, data visualization tools are more efficient because it allows the user to see a complete dashboard with a drill-down capability to gain a deeper and clearer understanding of the implications of their data analytics.

Data visualization allows healthcare providers to quickly identify meaningful trends. Here are the 4 key benefits of implementing data visualization:

  • Easily grasp more information
  • Discover relationships and patterns
  • Identify emerging trends faster
  • Directly interact with data

Figure 1: Payer Dashboard

Removing Departmental Siloes  

While data visualization does generate helpful insight into current and future trends, it begins with storing the data in one integrated system so that different departments can easily communicate regarding the data.

System integration is crucial to maintaining revenue integrity because it dramatically lowers the likelihood of data errors, missed reimbursement, and isolated decisions that don’t look at the full revenue picture. Here is a list of other issues associated with organizations running revenue siloes:

  • No consistent accuracy metrics driving performance and revenue.
  • Different data sources and systems drive independent and isolated decisions without known impact on the rest of the revenue cycle.
  • Departments cannot leverage analytics and insight into contract and payer performance.

In the spirit of the recent international World Cup games, think of revenue siloes like playing for a professional soccer team.

Similar to the structure of a hospital’s revenue team, soccer teams are large organizations that need to be able to clearly communicate with each other quickly in order to make calls on-the-spot. These quick decisions can be the difference in turning the ball over to the other team or scoring a goal in the final minutes so it’s crucial that everyone knows their role on the team.

If other players don’t understand the plays that are being called, however, then mistakes will be made that could cost them the game. Each player on the team needs to study the same playbook so they stay on the same page and decrease the chances that a costly mistake will be made.

A hospital’s Managed Care department works in a similar way. If Managed Care is preparing to renegotiate payer contracts, they need to fully understand and have insight into underpayment and denial trends across multiple payers.

Preparing Now for Consumerism and Price Transparency

Now that we know the reimbursement rate is accurate, how do we communicate an accurate price to patients in order to encourage upfront payment?

Studies have shown that by increasing accuracy in pricing estimates, it increases the likelihood that patients pay upfront, which can help your organization lower bad debt.

In an effort to migrate to a more patient-centric approach, these accurate online estimates also enable hospitals to address the patient’s fear of the unknown with healthcare of ‘how much is this procedure going to cost?’ By giving the patient more control over their financial responsibility, hospitals can become a leader in pricing transparency for their entire community while expanding on their market share.

At the end of the day, what this all comes down to is maintaining accuracy to help drive your revenue strategy. By integrating all data into a single system, the hospital is positioned to identify trends more quickly while increasing the accuracy of their patient estimates, ultimately driving your revenue strategy to new heights.

With many healthcare organizations still making the transition away from the traditional fee-for-service model, now is the time to prepare for consumerism and value-based care. Take some time to evaluate where your organization currently stands in the local market as well as any pricing adjustments that need to be made.

About Brad Josephson
Brad Josephson is the Director of Marketing and Communications at PMMC, a provider of revenue cycle software and contact management services for healthcare providers. Brad received a Bachelor of Arts, Public Relations and Marketing Degree from Drake University. He has worked at PMMC for over three years and has a deep knowledge of hospital revenue cycle management tools which improves the financial performance of healthcare organizations.

Hospital Using AI To Handle Some Tasks Usually Done By Doctors And Nurses

Posted on May 30, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

One of the UK’s biggest facilities has announced plans to delegate some tasks usually performed by doctors and nurses to AI technology. Leaders there say these activities can range from diagnosing cancer to triaging patients.

University College London Hospitals (UCLH) has signed up for a three-year partnership with the Allen Turing Institute designed to bring machine learning to bear on care, a project which could ultimately spark additional AI projects across the entire National Health Service. The NHS is the body which governs all healthcare in the UK’s universal health system.

UCLH is making a big bet on artificial intelligence, investing what UK newspaper The Guardian describes as a “substantial” sum to develop the infrastructure for the effort.

UCLH officials believe — like other health organizations around the world — that machine learning algorithms may someday diagnose disease, identify people at risk for serious illness and more. Examples of related projects abound. Just one case in point is a project begun in 2016 by New York-based Mount Sinai Hospital, which launched an effort using AI to predict which patients might develop congestive heart failure and offer better care to those who have already done so.

Professor Bryan Williams, director of research at University College London Hospitals NHS Foundation Trust, said the move will be a “game changer” which could have a major impact on patient outcomes. “On the NHS, we are nowhere near sophisticated enough,” Williams told The Guardian. “We’re still sending letters out, which is extraordinary.”

UCLH’s first AI effort, which is already underway, is intended to identify patients likely to miss appointments. Using existing data, including demographic factors such as age and address plus outside factors like weather conditions, researchers there have been able to predict with 85% accuracy whether the patient will show up for outpatient visits and MRIs.

Another planned project includes improving the performance of the hospital’s emergency department, which, like many NHS hospitals, isn’t meeting government performance targets such as maximum four-hour wait times. “[This is] an indicator of some of the other things in the entire chain concerning the flow of acute patients in and out of the hospital,” UCLH chief executive Professor Marcel Levi told the newspaper.

The hospital envisions solving its wait-time problem with machine learning. Drawing on data taken from thousands of patients, machine learning algorithm might be able to determine whether a patient with abdominal pain suffers from severe problem like intestinal perforation or a systemic infection, then fast-track those patients. This kind of triage is generally performed by nurses in hospitals around the world.

That being said, the partners agree that machine learning performance must be incredibly accurate before it has any major role in care. At that point, it will be ready to support clinicians, not undercut them. According to Professor Chris Holmes of The Alan Turing Institute, the whole idea is to let doctors do what they do best: “We want to take out the more mundane stuff that’s purely information driven and allow time for things the human expert is best at.”

Health Leaders Say Automating Patient Engagement Efforts Will Have Major Impact

Posted on March 12, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

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

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

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

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

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

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

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

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

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

Deep Learning System Triages Terminally Ill Hospital Patients

Posted on January 26, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Researchers at Stanford have developed a new tool designed to coordinate end-of-life care for critically ill patients. While the pilot study has generated screaming newspaper headlines (“AI tool predicts when people will die!”) researchers say that the system is best thought of as a triage option which helps hospitals and hospices provide timely palliative care to those who need it. It can also help terminally ill patients — most of whom would prefer to die at home — make plans for their passing and avoid dying in their hospital bed.

According to an article in tech publication Gizmodo, the Stanford set-up combines EHR data with other sources of information such disease type, disease state and severity of admission. The information is then processed by a form of AI known as deep learning, in which a neural network “learns” by digesting large amounts of data.

To conduct the study, researchers fed 2 million records from adult and child patients admitted to either Stanford Hospital or Lucile Packard Children’s Hospital. The system then identified 200,000 patients who met the study’s criteria. In addition to clinical criteria, the system also reviewed associated case reports diagnoses, number of scans ordered, number of procedures performed and other data.

After reviewing 160,000 case reports, the deep learning system was instructed to predict the mortality of a given patient within three to 12 months of a particular date using EHR data from the previous year. The algorithm included a requirement to ignore patients who appeared to have less than three months to live, as this window was too short for providers to make preparations to offer palliative care.

Then, the AI algorithm calculated the odds of patient death in the 3 to 12-month timespan extending from the original date. Its predictions turned out to be quite accurate. For one thing, it predicted patient mortality within the 3 to 12-month window accurately in nine out of 10 cases, a performance that few clinicians could match. Meanwhile, roughly 95% of patients considered to have a low probability of dying within 12 months actually lived beyond that point.

It’s worth noting that while the deep learning tool made fairly accurate predictions of patient mortality, the system doesn’t let healthcare providers know what treatment patients need or even how it makes its predictions. Luckily, researchers say, the system allows them to get a look at individual cases to better understand its deductions.

For example, in one case the system predicted accurately that a patient with bladder and prostate cancer would die within a few months. While there were many clues that he was near death, the system weighted the fact the scans were made of his spine and a catheter used in his spinal cord heavily in its calculations. Only later did the researchers realize that an MRI of the spinal cord most likely suggested a deadly cancer of the spinal cord which was likely to metastasize.

It’s worth remembering these results were produced as part of a pilot project, and that the predictions the system makes might not be as accurate for other data sets. However, these results are an intriguing reminder of the possibilities AI offers for hospitals.

An HIM Twitter Roundup – HIM Scene

Posted on December 13, 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.

For those that aren’t participating on Twitter, you’re missing out. The amount of knowledge and information that’s shared on Twitter is astounding. The problem is that many people think that Twitter is where you go to talk about yourself. Certainly, that’s an option if you want to do that, but I find that consuming information that people share on Twitter is extremely valuable.

If you’ve never done Twitter before, sign up (it’s free) and then you need to go in and follow about 50 HIM professionals and other healthcare influencers. You can start by following @healthcarescene. HIM professionals are easy to find. Just search for the term AHIMA or ICD-10 and you’ll find a lot of them to follow.

Ok, enough of the Twitter lesson. Just to show you some of the value of Twitter, here’s a quick roundup of HIM related tweets. Plus, I’ll add a little commentary of my own after each tweet.


This is becoming such an important role for HIM professionals in a healthcare organization. HIM staff can do an amazing work ensuring that the data that’s stored in an EHR or other clinical system is accurate. If the data’s wrong, then all these new data based decisions are going to be wrong.


I think upcoding stories are like an accident on the freeway. When you see one you just have to look.


I’m still chewing on this one. Looks like a lot of deep thoughts at the AHIMA Data Summit in Orlando.


The opioid epidemic is such an issue. We need everyone involved to solve it. So, it’s great to see HIM can help with the problem as well. I agree that proper documentation and EHR interoperability is a major problem that could help the opioid epidemic. It won’t solve everything, but proper EHR documentation is one important part.


This is an illustration of where healthcare is heading. So far we’ve mostly focused on data collection. Time to turn the corner and start using that data in decision making.

UPMC Plans $2B Investment To Build “Digitally-Based” Specialty Hospitals

Posted on November 20, 2017 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

The University of Pittsburgh Medical Center has announced plans to spend $2 billion to build three new specialty hospitals with a digital focus. Its plans include building the UPMC Heart and Transplant Hospital, UPMC Hillman Cancer Hospital and UPMC Vision and Rehabilitation Hospital. UPMC already runs the existing specialty hospitals, Magee-Womens Hospital, Western in Psychiatric Institute and Clinic and Children’s Hospital of Pittsburgh.

UPMC is already one of the largest integrated health delivery networks in the United States. It’s $13 billion system includes more than 25 hospitals, a 3-million-member health plan and 3,600 physicians. If its new specialty centers actually represent a new breed of digital-first hospital, and help it further dominate its region, this could only add to its already-outsized clout.

So what is a “digitally-based” hospital, and what makes it different than, say, other hospitals well along the EMR adoption curve? After all, virtually every hospital today relies on a backbone of health IT applications, manages patient clinical data in an EMR and stores and stores and shares imagines in digital form.   Some are still struggling to integrate or replace legacy technologies, while others are adopting cutting-edge platforms, but going digital is mission-critical for everyone these days.

What’s interesting about UPMC’s plans, however, is that the new hospitals will be designed as digitally-based facilities from day one. UPMC is working with Microsoft to design these “digital hospitals of the future,” building on the two entities’ existing research collaboration with Microsoft and its Azure cloud platform.

The Azure relationship dates back to February of this year, when UPMC struck a deal with Microsoft to do some joint technology research. The agreement builds on both UPMC’s fairly impressive record of tech innovation and Microsoft’s healthcare AI capabilities, genomics and machine learning capabilities. For example, in working with Microsoft, UPMC gets access to Microsoft’s health chat bot technology, which is being deployed elsewhere to help patient self-triage before they interact with the doctor for a video visit.

I’d love to offer you specific information on how these new digitally-oriented will be designed, and more importantly how the functioning will differ from otherwise-wired hospitals that didn’t start out that way, but I don’t think the two partners are ready to spill the beans. Clearly, they’re going to tell you all of this is the new hotness, but nobody’s provided me with any examples of how this will truly improve on existing models of digital hospital technology. I just don’t think they’re that far along with the project yet.

Obviously, UPMC isn’t spending $2 billion lightly, so its leadership must believe the new digital model will offer a big payoff. I hope they know something we don’t about the ROI potential for this effort. It seems likely that if nothing else, that technology investment alone won’t drive that big a rate of return. Clearly, other major factors are in play here.

Healthcare Execs See New Digital Health Technologies As Critical To Success

Posted on October 30, 2017 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Healthcare organizations have spent massively on HIT in recent years, in hopes of preparing for success by building next-generation tech infrastructure.  If a new survey is any indication, while the current set of efforts haven’t born as much fruit as their leaders like, they remain hopeful that the next wave will better support their goals.

The SAP Digital Transformation Executive Study, which surveyed about 400 healthcare executives, looked at whether the healthcare industry was prepared for the digital economy.

Respondents told SAP (and survey partner Oxford Economics) that the existing technology investments weren’t delivering the value they wanted, with only 22% saying they supported customer satisfaction efforts and 23% saying that they helped foster innovation.

Fortunately for health IT vendors, however, that wasn’t the whole story. Perhaps because hope springs eternal, healthcare leaders predicted that in two years thing should look different.

In fact, 70% said that the latest technologies were essential to growth, competitive advantage and customer experience. In two years, 61% expect technology investments to boost customer satisfaction, and 59% believe the technologies will help support innovation.

This may be, at least in part, because many healthcare organizations are in the process of kicking off digital transformation efforts and are relying on new technologies to achieve their goals. Though the process hasn’t advanced too far in many organizations, respondents all seem to be making some progress.

According to the survey, healthcare execs expect the importance of digital transformation to climb over the next several years. While 61% said it’s important today, 79% expect it to be important in two years and 86% believe that it will be important in five years.

To prepare for these eventualities, 23% of respondents said are planning digital transformation initiatives and 54% are piloting these approaches. In addition, 32% reported that their efforts were complete in some areas and 2% said their process was complete in all areas. Almost half (48%) said a lack of mature technology was holding back their efforts.

When asked to name the technologies they expected to use, 76% of healthcare leaders predicted that big data and analytics will help them transform their business. They also named cloud computing (65%), IoT technologies (46%) and AI (28%) as tools likely to foster digital transformation process.

I don’t know about you, but personally, I’d be pretty upset if I’d spent tens or hundreds of millions of dollars on this wave of health IT and felt that I’d gotten little value out of it. And given that history, I’d be reluctant to make any new investments until I was confident things play out differently this time.

Under these circumstances, it’s not surprising that healthcare execs are taking their time with implementing digital transformation, as important as this process may be. With any luck, the next wave of digital technology will be more flexible and offer greater ROI than the previous generation.