When Should Health Systems Invest in New Tech?
From AI to digital apps, new technologies are proliferating in health care. It is very easy for leaders of health care organizations to be seduced by them. They should resist. Instead, they should be disciplined in deciding which ones to adopt and which ones to resist and how to incorporate them. Five steps can help: Focus on the transformation you wish to achieve, not the digital; Understand why a particular technology may be an important tool in achieving a specific goal; choose suppliers wisely; engage in iterative learning; sustain the transformation by establishing an organizational infrastructure.
In my long tenure as CIO at a large academic health system, I was often accosted by senior members of the medical, nursing, or administrative staff, just back from a meeting: “I saw a demo of this next-generation electronic-health-records system. Unbelievable! It saves lives, reduces costs, improves the patient experience, and mows lawns! We need to implement it here! It would transform us!”
Or a board member would weigh in: “We need to be aggressively pursuing artificial intelligence! AI will be incredibly disruptive, possibly replacing most of our physicians. I heard that our rival health system is working closely with a tech giant and if we are not careful, we will be out of business!”
These conversations always left me with a sinking feeling. It’s not that I don’t like technology. What I don’t like is technology considered in a vacuum, without regard to the bigger picture. Or worse yet, colleagues and superiors so seduced by what I call “shiny objects” that they’re willing to throw that carefully painted bigger picture into chaos. A Rembrandt becomes a Jackson Pollock just like that.
AI, the next-generation electronic-health-records (EHR) system, or any number of other shiny objects brought to my attention in this way might be very important. They might advance the organization’s strategies beautifully. They might enable strategies that we hadn’t even thought of before. They might be the key to our survival.
Or they might not. They might waste money and time, divert us from our goals, and allow more disciplined competitors to eat our lunch with wiser technology choices and better implementation. The next-generation EHR — so compelling in demo mode — might lock up at random times once it’s crunching real data. The AI that caught the board member’s eye might make recommendations that enrage the medical staff — or worse, mystify them.
I had to develop a way to determine whether a shiny object had significant potential or was a looming train wreck — an approach I will share in this article.
Successful tech implementations get all the attention; everyone wants to be the next Uber, Netflix, or Amazon. But they are outnumbered by the failures. Organizations large and small have fallen prey to the lure of shiny objects, whether rushing into ecommerce without thinking things through, deploying technology that rapidly becomes obsolete, or over- or underestimating customers’ interest in doing everything via an app. (This article is a nice chronicle of how big name companies came to digital grief.)
Sometimes what sounds like a good idea is a bad idea. Google thought that its Google Glass augmented reality headset would be a hit with doctors, who could use it to look at test results while examining a patient. Instead, it proved distracting to both doctors and patients and raised privacy and security concerns.
Sometimes even if the tech is ready, an organization may not be. Thousands of health care providers rushed to comply with the 2010 federal mandate to adopt electronic health records and take advantage of the subsidies available only to find that they had murky understanding of how to use the technology to improve care.
How do we determine which shiny objects will fit into the network of stable, healthy relationships that make up an effective digital transformation and which ones will leave us broken-hearted and fodder for a case study of what not to do? How do we resist the siren call of an amazing demo and, instead, suggest our leaders take a cold shower when they get into an advanced state of technology arousal? Here are some suggestions.
Digital technologies are only valuable to the extent that they can be effectively applied to achieve organizational goals. So of course, first you need to have organizational goals that are sturdy enough to both sustain a transformative technology and resist the temptation to adopt an irrelevant one.
Suppose a health system embraces these two goals:
How does it use these goals, this vision, to assess whether a digital technology is worth pursuing? Being both a provider and a payer changes a health system’s economic equation. Because it’s billing itself for the care it provides, any technology has to help it deliver both better care and lower cost. And because it aspires to give its patients a world-class experience, its push for efficiency must also include personalization to keep patients from feeling like they’re getting cookie-cutter care.
Artificial intelligence could help with both those goals. It could analyze EHR and insurance claims data to look for treatment patterns that both improve outcomes and lower costs and then recommend changes in treatment protocols. At the same time, AI could be used in patient-facing applications to tailor patients’ experiences to their clinical conditions, language skills, and care preferences.
However, for any number of wonder digital technologies, the relationship between their capabilities and organizational goals is forgettable flirtation.
What can a specific technology do that would make it potent in accomplishing a goal? A health system should be able to state in one or two sentences the core potential contribution of a technology.
For example:
The statement of capabilities helps leadership understand why the technology might be important as well as appreciate the full range of potential uses: recognizing patterns in data such as conversational voice, analyzing radiology images, tracking medication-purchasing patterns to flag disease outbreaks, and potential medication side effects. A truly potent technology will spread beyond its initial application.
Equally important are statements of possible limitations that can thwart the achievement of significant value. For example, AI can suffer from bias and an inability to explain its reasoning.
By the way, digital transformation does not always require the latest and greatest technology. Sometimes proven and mature technologies are very much up to the task of enabling the organization to create a brilliant, digitally-enabled future. An early and (by today’s standards) crude iteration of AI featured in most EHRs — clinical decision support (CDS) — sends clinicians suggestions and reminders and flags relevant recent research based on the information in the patient’s record. While CDS can drive a clinician up the wall with too many reminders, a properly deployed system can transform care. For example, many hospitals sharply reduced the number of patients who developed pneumonia while on a ventilator by adopting a bundle of standard practices to prevent infection and using their CDS to remind clinicians to use those practices.
Once you’ve decided that a shiny object is worth pursuing, it’s time to sort through the many vendors who’d love to sell you their particular version of it.
Ignore buzzwords: disruptive, solution, platform, ecosystem, cloud. Make the vendor tell you what the shiny object does and what it has done for organizations like yours. (If yours is the first, that’s a whole separate discussion.) Insist on detailed explanations of how the shiny object works and demos that give your staff a chance to try to break the object. Do reference checks — preferably with customers that aren’t on the standard reference list. Determine to the best of your ability whether the vendor is likely to survive for the full duration of any proposed contract. If appropriate, assess whether the technology is subject to regulatory requirements.
Shiny objects can sometimes show up dings, dents, and dirt in their surroundings. For example, as you apply your newly acquired AI capability to identify treatment effectiveness, you may have to finally face up to and deal with the uneven, often poor quality of your EHR data. Maybe your physicians have been relying on the “notes” field instead of checking the boxes. Maybe key pieces of data end up in the wrong fields or are not documented at all. Understanding and fixing these issues is crucial in order to reap the full benefits of your new AI — or possibly to get it to function at all.
The most transformative technologies usually take a while, and require a number of iterations, to demonstrate their value and the change-management steps needed to achieve that value. A sage colleague once told me that leaping too far too fast can lead to “a death by ants.” No single bite will kill you, but a thousand will. No matter how big the step, some things will go wrong. If the step is too large, too many things go wrong, and real damage can result.
Take a step or two and then assess. Then take another step or two and reassess.
Remote patient monitoring — sensors that send vital data from patients in their homes to the hospital or clinic — is one of the shiniest objects in health care tech right now, made all the shinier by Covid-19 and the need to minimize direct contact and office visits. One of my MBA students described a hospital customer that was using his company’s technology to monitor the rehabilitation of middle-aged male patients recovering from heart attacks. A mobile app captured sensor data and patients’ own reports on diet and exercise. This data was transmitted to the care team who monitored the patient’s progress.
Through pilot testing, it became apparent that though the technology was slick and transmitted information as it should, the patients’ success or failure rested on how the care team responded to the information. Words of encouragement when patients reported regular exercise sessions and words of concern when their weight started to creep up had more of an impact on compliance than the mere capture and reporting of monitoring data. The director of the rehab program said the most important success factor was the degree to which the patients felt that the team cared about them.
The team that was leading the pilots of the technology was reminded of the holistic context of patient care — the word “care” is there for a reason.
To create and sustain an environment friendly to digital transformation, an organization needs a person, a unit, or a department (depending on the size of the organization) to continuously review new technologies and oversee the steps above.
When I was a CIO at Partners HealthCare, an academic health system that’s now called Mass General Brigham, we established, within the information technology department, several centers that focused on key aspects of artificial intelligence. One center determined the potential uses of large volumes of EHR data for clinical research, post-market surveillance, and determining comparative effectiveness of different treatments. Another center created the technology infrastructure necessary to implement complex clinical decision support and artificial intelligence, and evaluated the impact of these technologies.
These centers, funded by the health system’s operating budgets and grants, ensured that there was a sustained, multi-year focus on understanding and implementing digital technologies considered to be particularly important to the health system’s strategies.
Creating sustained digital transformation requires recognizing three realities. First, transformation never stops. Second, neither does technology innovation. And third, shiny objects are everywhere. Technology seduction will be a constant threat to any organization that lacks a clear sense of what it wants from technology and why.
When Should Health Systems Invest in New Tech?
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