Introduction

Like Consumers, Patients – consumers of healthcare services – have evolving and increasing expectations from the providers and payers. Supplier driven processes and hence systems driving the processes must give way to a patient centric, highly integrated, smart, and responsive systems that work towards delivering patient centric care which ensures best possible outcomes, lower costs and best patient experience.
Providers and payers need to evolve their models, processes, technology, culture and positioning to adopt emerging paradigms such as value based care.
Technology – specifically Big Data, Cloud, IoT, Mobile, and AI will play a pivotal role in enabling the transformation of healthcare towards patient centricity. AI enabled insights, AI and IoT enabled automation, cloud enabled integration and mobile enabled accessibility will be at the core of enabling the transformation.

Data: A Key Enabler

Following are some of the use-cases/product categories that have emerged in the recent years to aid or accelerate the transformation:

  • Clinical AI
  • Augmented EHR
  • Conversational AI for Healthcare
  • Tele-consulting
  • Voice AI for Healthcare
  • Virtual Health Assistant
  • Health Wearables
  • Healthcare IoT

….and so on

A common minimum denominator in these emerging technology driven solutions is Data – a smart and highly automated solution is almost always a data hungry solution. Similar to Customer 360 which makes delightful customer experience – across all channels – a priority and makes use of data and data-driven smart automation as its key enabler, a patient centric healthcare can only be achieved through integration of patient and auxiliary data gathered at various touch points throughout the patient journey and beyond.

However, there are real-life challenges that the provider and other ecosystem participants face to ensure availability of quality data to power the next generation solutions.

Challenges

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  • Complex Healthcare Ecosystem

Healthcare ecosystem is a complex maze of independent businesses and modalities which often result in data silos. To enable patient centric healthcare, the provider(s) must find a way to integrate the data collected at various sources and touch points.

  • Integration Projects are Costly

While it would be unfair and disingenuous to term the efforts to standardize information exchange as a failure, it would also not be an exaggeration to say that there has been disappointment despite the huge investments.
Interop mechanisms did a fine job in defining the contours of the information exchange and sought to standardize a lot of aspects however several providers ran (and still run) highly customized implementation which means any integration or interoperability projects are inherently complex and time consuming.

  • Interop Mechanisms are Outpaced

In the last couple of decades, huge investments have been made to enable and improve interoperability of various healthcare providers and ecosystem participants.
However, the mechanisms, standards and processes governing the interoperability have been outpaced by the advances in healthcare technology, technology is general and growing expectations of the patients.

Data Integration: Foundation for Patient Centric Healthcare

Following are the facets of approach towards effective data integration and processing system to lay the foundation for delivering the Patient Centric Healthcare:

  • Extensible/Pluggable and Multi-Paradigm

Healthcare data integration and processing workloads today are a combination of multiple paradigms – streaming, batch, and complex event processing (CEP), and the pace of innovation in the Software, IoT and Wearable healthcare means that the demand for adding more sources will be a constant. Hence, it’s important to have a system which is extensible and pluggable, and supports different types of workloads.

  • Scalable and Elastic

With the ever increasing data and disparate workloads, data integration and processing systems must scale horizontally and elastically to respond to the data integration and processing scale demands while being efficient in the resource consumption.

  • Standard (HL7, ICD, CCD, DICOM…) Aware

Healthcare information exchange standard – HL7, ICD, CCD, DICOM… – aware data integration and data preparation – validation, cleansing, and transformation – capabilities are critical to the success of the data integration efforts. This ensures the data exchange – if not interpretation – with the other ecosystem participants remains stable and robust.

  • Data Mastering

Healthcare datasets are notorious when it comes to the problem of data mastering. The system dealing with the data needs to reconcile records from a myriad of systems and organizations. Difference in the processes, taxonomies, and hence the implementation of systems of record makes this job extremely tedious, time consuming and more worryingly error prone.
A robust human in the loop data mastering and governance system – technology, tools and processes – is a must to ensure success of the data integration efforts. Scalable smart data wranglers, entity matching algorithms and tools to run mastering workflows can be employed to achieve the mastering of data.

  • Security and Compliances

Healthcare security, compliances, data  policy and governance frameworks are required to make sure that sensitive health and identity data is protected from internal and external information thefts.
Healthcare space will rapidly evolve, especially with the advent of AI and IoT. Although the impact of the still on-going pandemic is yet to be fully understood, most expect the innovation in the healthcare space to only accelerate. Irrespective, data – as a direct insight provider and as an automation enabler – will continue to dominate the space for time to come.
Talking of data and integration, you can also check out one of our earlier blog post which captures the approach towards data integration in the wake of hyper-adoption of SaaS apps and Machine Learning/AI & Analytics.