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‘Insight to Eyesight’: How effective clinical decision support achieves high-quality healthcare

Get contextually relevant, research-informed and actionable information to the clinician so they can make a decision about the person who is in their care.

This is the goal.

Get ‘insight to eyesight’.

Barriers

On the surface, it seems like a very simple thing to do. One day, we will look at how our technology and processes are structured and think it really is quite simple.

What is required in the immediate future is for the healthcare community to embrace the technology capabilities that have been developed to date and put additional focus on the next two frontiers: Enterprise Decision Support and User Experience. This adoption will help us achieve our goal.

Clinicians have long suffered the effects of proprietary data models, lack of interoperability, short-sighted policy, bad technology design and broken workflows. These dynamics have forced clinicians to perform heroics and search for information in disparate systems like needles in a haystack.

Many clinicians are inundated with mass notifications from poorly designed systems, causing alert fatigue and apathy. The potential consequence of not finding that needle, or ignoring an alert, could be the death of their patient. This is simply not fair to clinicians who put us, their patients, first.

We should be implementing standards, policy and technology, which puts their needs first and makes it easy for them to do their job. The good news is that the healthcare industry has come a long way to achieving this goal. This seemingly futuristic state is already here, it is just not evenly distributed.

Progress

The big data revolution has forced organisations to recognise the need for a common way to store the data that underlies how they function. Healthcare has been no different. Data can now be collected and stored in formats which can be used across the lifecycle of a patient’s interactions with a healthcare system without issue.

Once the data issue was addressed, interoperability became the next barrier. How can we get a patient’s data to follow them through their care journey so the clinicians they interact with can use it? Fortunately, interoperability has progressed to the point where this problem has a solution. The criticality of interoperability and its value was very well summarised in the recent report issued by the New Zealand government. The NHS has also published a toolkit to help this future state be scaled out faster.

Once interoperability is achieved and the technology allows for information to flow, a simple yet critical policy decision needs to be made: Do we want to support the practice of data liquidity?

It is refreshing to see tech and healthcare giants step up and commit to the flow of information and articulate its criticality in establishing the requisite playing field for advancements in Artificial Intelligence (AI) and Machine Learning (ML).

We also heard very strong language in a recent webinar from the ACT that took this concept further, even hinting at potential laws enforcing the requirement to exchange health data for the sake of the patient. NSW Health’s desire for a Single Digital Patient Record also continues down this path in a very positive way. While Australia’s My Health Record has had a difficult journey, it provides an excellent foundation for future health record sharing.

Now that we have a place to put the data, the means to exchange it and the policy to allow it – what’s next?

What’s next?

First, we need a layer of logic that curates the most relevant and important information required by the clinician. This logic can come from many sources:

  • Rules based on best practice research
  • Protocols established by peak bodies relating to practice standards
  • The more advanced use cases of AI and ML

This layer also requires a toolset that easily allows an organisation to implement the logic, pushing the resulting information to a system which can present it to the user in a meaningful way.

While it is still early days, the CDS hooks approach will help us achieve this goal of logic flow. This layer needs to be agnostic of the various systems used across an enterprise, to prevent an organisation from being locked into a proprietary approach of the logic’s application and use cases. The logic must also assess whether it is being effective in its application, to create a constant feedback loop and improve future interactions with the users of the systems it serves.

Second, we need advancements in what might be considered the most important layer of all – the user interface. Every time a clinician’s eyes look at a piece of technology, those seconds are critical and should present only the most important and relevant insights to help them make a judgment call about the person they care for, and it should be at the point when and where they need it.

The technology should be a supporting mechanism for the memory function of the clinician, so they can perform at the level of their training and make the decisions necessary to keep us safe and healthy in their care. The design of these user experiences must be able to be iterated with agility until we achieve true adoption and meet the clinician in their workflow.

A good user centred design, coupled with an advanced Decision Support Engine, are two giant leaps of capability that will allow us to achieve our goal of getting insight to eyesight. Once these capabilities are in place, we will have the tools required to constantly improve the effectiveness technology delivers for the clinicians and our current goal will be achieved.

The question remains – exactly when will the healthcare sector demand these capabilities as mandatory requirements of their technology providers? We must embrace and deploy the foundational capabilities, so collectively we refocus on the more transformational elements of healthcare delivery in Enterprise Decision Support and User Experience.

Steve Lutz is General Manager of Business Development for Australia and New Zealand at Alcidion