There are some problems related with Digital Health Transformation. It is so normal in our digital age, the focus on technology is highlighting developments and innovations. When
modernizing healthcare however, this emphasizes on technology is overshadowing the real centre of where change needs to happen… people.
Digital health transformation is by many presumed to be about transferring vast amounts of data and having technology as “ready to go” solution.
Healthcare however is a people business, centring around the needs and desires of humans giving and receiving care. This makes that when it comes to making digital health transformation happen, the difficulties do not lie with building the right technology, but with the behaviour changes that need to happen.
This makes that first step to successful digital health transformation is to understand the problems of digital health transformation lying underneath.
Understand the problem
This seems very simple but is often harder than you think. For example, when
healthcare providers would like the system of patient recording to change from let’s say paper to electronic. It might seem like you have fixed the problem by implementing the newest electronic system that enhances access, with lots of different options and shining innovative attributes.
However, when asking why they want the system changed and what the specific problems are with the current system, you might find out that for care givers a simple system, with fewer options and a user friendly interface is key to perform their jobs effective and efficiently.
In that case,
your beautiful new system would be far from desired.
“It’s about finding the pain points and determining how that translates to the needs of caregivers or patients.” – (Travis Good, 2017)
Make the technology fit into the workflows
After you have a clear understanding of the problem it is key to gain insight into how the technology will fit into the
workflow of the healthcare provider. Try looking at the work as an
operation project rather than an IT project. This ensures that the technology will be aligned with the strategic initiatives and objectives of the overall department.
An example, a project around operating procedures replacing hips and knees. Having an IT perspective we might focus on gathering the data from the electronic health records to improve the inventory levels of replacement joints which boosts the return on investment by sterilizing fewer equipment, reduction in employee costs and other efficiencies.
This of course would already help a lot. However, taking an operation project perspective and while speaking with operational people, the director of the OR for example, you might find out that there are additional department goals which could be well implemented in this system. Think about accurate and timely data to help with budgeting or supply chain management.
Standardize your Digital Health Transformation
Healthcare systems are often highly complex and interlinked with various systems and processes. Therefore, to successfully implement digital health, standardization is key.
The technology tool itself but also the processes and support systems around it, such as training and compliance need to be interlinked and standardized. This can reduce complexity each time another application is added to the ecosystem and therewith creates system sustainability.
When deciding whether or not to add a new technology,
standardization should be applied as well. A piece of technology can be highly innovative and change the way care is provided, however the new IT might not be suitable to be built into the existing system. There should be a process in place to understand whether it is a smart long-term investment.
The
digital health conversation is dominated by technology but most people working outside IT do not know what is possible in this field. This makes that for doctors for example it is hard to formulate what exactly the technology should do.
Using the technology such as documentation tools for people to type into, costs them time away with patients. Especially since IT people build the systems without a clear value proposition,
healthcare professionals complain since they have all the data but are not using it.
For this reason discussions needs to change. More non-technological and subject related experts need to sit around the table. Information need to be gained from the people technology is serving, patients, doctors and nurses. What are they experiencing, what would truly benefit them and how would it fit their needs, desires and ways of working.
“In the short term, having more voices around the table won’t necessarily make the process of transformation more efficient. But, in the long-term, broadening beyond a technology discussion will be necessary.”–
(Travis Good, 2017)
If we truly want
successful digital health transformation and receive the benefits from such systems, we need to go back to the before the time of the tower of babel and try to speak the same language, understanding each other.
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Furthermore. Sleep trackers cannot differentiate between light and day and could be tracking the wearer as asleep when they are in fact just resting. Ultimately, it has been observed that sleep trackers aren’t always accurate.
Remember that sleep trackers like other activity trackers are wearable digital devices that measure, amongst other things, your arm movement with a detector called an accelerometer. So it is entirely possible that the sleep tracker is indicating you are asleep when you are in fact, not.
If you are struggling with a sleeping disorder such as insomnia, a sleep tracker will only tell you how much sleep you didn’t get and is not sensitive or sophisticated enough to diagnose the problem. Ultimately, it may keep people from seeking the medical attention they need to fully diagnose if they have a sleeping disorder which may be detrimental to their overall health.
Furthermore, those who are tracking that they are getting a full 8 hours of sleep may be misled that they have no sleep disorder, when in fact, they do. Their tracker may indicate they slept for 8 hours, but it will not always accurately track if they were restless or had brief moments of awakening.
The bottom line is that too many people may be relying too much on the numbers that their wearables are recording and not on the actual quality of their sleep. Are they waking up refreshed and feeling restored? Are they energized or did they wake up more tired than before they slept because their sleep was restless and disruptive? Then, Can Wearables Be Detrimental To Our Health?
The amount of stored patient data has increased with 700% between 2010 and 2015, 91% of that data is unstructured. This data resides outside of an organised database such as lab records or electronic health records, making it impossible for payers, care providers and patients to tap into its potential. However, if we would be able to tap into this data we could accelerate healthcare transformation, making it more efficient and cost-effective.
Therefore, Artificial intelligence (AI) is seen as the solution to the problem. With its capability to draw “intelligence” from the vast amounts of raw data, located in different sources, it has healthcare executives worldwide supporting it. Globally, 63% of healthcare executives are already investing in AI technologies and 74% are planning to do so as well.
A PwC study found that healthcare is most likely to derive some of the biggest gains through AI. It would increase productivity, enhance the quality and give the space to the increased consumption.
The possibilities would be endless, especially with regards to diagnostics. It could for example:
Thus, AI can safe vast amounts of cost and increase efficiency throughout our healthcare system. Moreover, healthcare executives, clinicians and patients are all willing to try and are seeing its potential. Does this mean that AI is our health future?
Yes, I think it is inevitable. Studies are already predicting that the AI market is in for massive growth. Between 2014 and 2021 a 40% increase in the market of AI in healthcare is expected, growing from $633.8 million to $6.66 billion.
However, while the potential is surely there, there are still many unknowns and issues to be solved before our health AI future can reach its full potential. New systems and business models will need to be implemented and large investments are needed to make everything happen.
I think the below sums it all up…
“The road toward the vision of a self-serve, consumer-directed, personalized healthcare delivery model powered by artificial intelligence will likely be repeatedly disrupted by unexpected stops and starts. But, to more and more clinicians and technologists on the leading edge of healthcare IT development, it’s just a matter of time.” – (Brian Williams, 2017)
And that it is, we will need to work hard, but our AI healthcare future is soon to be there!
This article is based upon the following post:
http://usblogs.pwc.com/emerging-technology/ai-in-healthcare/?utm_content=bufferb9882&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
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