Healthcare transformation: 7 steps you can't skip Sponsored by googlecloud
The pandemic revealed the importance of interoperability in healthcare. Learn how the right digital foundation is essential to facilitating communication more easily and handling complex analytics to ensure better outcomes, efficacy, and equity.
.In legacy architectures, data and functionality from one application could not be easily re-used or connected to another application, and updates to one part of an application risked breaking data or functionality somewhere else.Decoupled systems have supplanted these legacy approaches
, enabling digital services to be modularly assembled from digital assets across a variety of sources—some data from one place, some data from another place, some functionality from yet another place, and so on. APIs enable these kinds of distributed architectures because they abstract backend complexity from frontend development. This decoupling is how interoperability occurs—and it’s how data from one organization might be combined with data, platforms, or AI services from another. This kind of granularity is core to a “
cloud-native” architecture, and to healthcare organizations applying cutting-edge cloud services to different kinds of data. If an organization embraces cloud-native architectural principles, it can not only connect a legacy, on-premises system to a cloud-hosted service but also more impactfully move from on-premises systems to the cloud in order to take advantage of better agility, reliability, scalability, and security. headtopics.com
2. Interoperability stack:Interoperability starts with APIs and FHIR compliance, but excellence in this area goes much deeper. The ability to harmonize structured and unstructured data into standard formats such as FHIR formats, for example, is a significant step beyond just creating APIs. Likewise, having APIs means being able to connect data to analytics and AI tools—and robust interoperability means these connections are reliable and scalable, rather than bespoke, with the ability to consistently apply analysis and visualization tools across datasets.
3. Protection of patient data: Interoperability blends the need for private patient data to stay private while allowing data to be shared across healthcare professionals and institutions. Hospitals often use an extract-based approach that requires many copies of the data outside of the database—a potential security flaw. Instead, developers and healthcare professionals should be able to analyze data where it resides, all while observing
and HIPAA compliance.4. Health equity by design:We can’t improve what we can't measure. IT teams must take a proactive approach to incorporating and building systems that address disparities in how we collect data, measure and analyze the findings, and tailor interventions. As an example, clinical data about patients that exist within EHR alone is not sufficient to determine health equity needs. It is a substantial effort to build a data ecosystem to get a deeper perspective, including social needs data, public health datasets, self-reported experiences, and outcomes data
in additionto race, ethnicity, and language (REaL) data. Data and analytics are key pillars to help identify disparities for marginalized, minority, and underserved communities.5. Minimizing latency: Hospitals frequently rely on faxes and weekly reports for analytics, but waiting a week for the data to aggregate often is far too long. When making life and death decisions, access to real-time information is critical. As soon as information is recorded in a system, it should be transformed into a consumable format such as FHIR so that it can be combined with other sources, analyzed with ML tools, and so on. Updates should propagate to users in seconds, minutes or hours—not weekly. These other sources are going to dramatically increase with the advent of those outside clinical settings, such as in-home monitoring scenarios. headtopics.com
6. Building for scale: The scale and scope of hospital data is rapidly increasing: hospitals are tracking more clinical events; clinicians are incorporating data from not only the lab but also devices used in homes; individual hospitals are consolidating into larger systems; more patients are using technology as an intermediary to negotiate interactions between themselves and clinicians; etc. Accommodating all of these requirements at scale demands not only cloud-native approaches but elastic compute and storage services, cloud-hosted ML services, and more.
7. Continuous availability: When an organization relies on staffed, on-premise data centers, it necessarily incurs significant expense and IT resources to keep everything running. Managed cloud infrastructure and services, in contrast, let organizations meet uptime requirements while allocating their in-house IT talent more flexibly.Read more: Forbes »
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