S P E C I A L I S T I N S I G H T

Historically , technology has added a lot of burden and made things inefficient for clinical teams – EHRs being a great example . We now have the opportunity to use approaches and ideas for new technology in healthcare that are typical in other industries , such as human-centred design .

If engineered with users and clinicians in mind , we can create tools that lead to better clinical workflows in areas that have become more complex , such as clinical documentation . This can allow clinicians to be clinicians again and focus on taking care of their patients . By looking at the end user and the real purpose behind it , we can reimagine technology in ways that will actually solve many of the problems that we ’ ve created over the last 15 – 20 years .

In what ways can HealthTech be utilised to address disparities in population health outcomes , and what innovative solutions have you encountered that show promise in this area ?

How an individual ’ s disease state progresses is not entirely dependent on the clinical transaction ; non-clinical factors , such as ethnicity , education , income level , family and community support structures , etc . impact an individual ’ s health significantly . It ’ s not enough to just examine the feet of a diabetic , check the box and move on .

What strategies do you recommend for achieving seamless clinical and information integration in healthcare systems , and what potential challenges should organisations anticipate ?

To achieve clinical and information integration , human beings need to interact with technology in humancentered ways , such as improving clinical attention rather than worsening it through information overload . Technology alone rarely solves problems in complex environments and can end up just replacing one inefficient workflow with another if not designed properly . IKS believes strongly in the idea of keeping a ‘ human-in-the-loop ’ where technology augments and supplements what humans sometimes are not good with .

The right way to design it is to consider who actually needs the information and when they need it . For example , in my clinical practice I get a report every time one of my patients who has a pacemaker gets his / her pacemaker evaluated . A cardiologist may need to know this , but I do not as the primary care provider . Right now , everything is sent to the PCP , filling their inbox with information that is irrelevant to them on a day-today basis . It makes it difficult for clinicians to focus on patient care . We need to rethink workflows and leverage technology to enable the right information getting to the right person at the right time .

How can HealthTech support the transition to value-based payment models , and what are some examples of technology-driven success in this area ?

Technology can be , and is starting to be , an enabler of providing a comprehensive view of the patient through information integration . Population health management tools can provide insights beyond standard clinical measures . These tools have become quite sophisticated , collecting data from a large number of disparate sources , factoring in social determinants of health , and enabling care teams to come up with personalised , holistic care plans . There is technology now that integrates directly with population health tools to identify risks by looking at factors such as exposure to environmental toxins by zip code and other nonclinical data .

Value-based care is different from traditional models in both how care is delivered and how it is paid . Technology needs are quite different between the two models of care . Instead of supporting transactional medicine , technology platforms for VBC need to provide longitudinal patient insights , support analysis at population cohort level and enable measurement of cost and quality outcomes .

Most healthcare systems are not equipped to handle this . Value-based care organisations are now retrofitting their technology platforms to do just that . They are adding a layer of information integration between longitudinal patient data , and quality and cost outcomes . This also requires the creation of data warehouses and data lakes ,

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