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The proliferation of Digital Health technologies in the life sciences and healthcare industry promises the shift to a patient-centric approach to diagnose, treat, and predict diseases. Imagine a scenario in which a patient can predict when they will need to go to the doctor before a cardio-vascular condition could set in.

With today’s ubiquitous technologies such as smart phones and connected devices such as watches, scales, oximeter etc., patients could make their own health decisions without the need to consult with their doctor.  However, until we have a “Digital Health Twin” on our smart phones that could assess our complete health status as well as our family disease histories, there is still a long way to go. The future of Digital Health depends on access to patient data and the seamless interoperability of data on digital systems in the healthcare ecosystem. 

Data in the life sciences and healthcare industry originate from multiple entities (see Figure 1), serving multiple purposes. Patients’ healthcare-related data sets are typically referred to as Patient Health Data. This includes clinical data, prescriptions, electronic health records (EHR), clinical trials data from pharma organizations etc. as well as family records. Such data originate from hospitals, healthcare professionals and pharmaceutical organizations. Digital technologies allow patients to bring in their near real-time data captured using personal devices such as watches, and IoT devices. These real-time data usually reside within different departments of healthcare or consumer health organizations that may not fully exploit their intrinsic value. Patients’ data that reside outside of their primary care unit are called Healthcare Ecosystem Data, and include data from pharmacies, laboratories, imaging centers, R&D organizations, and public health organizations. This cross-organizational data operability is critical to unlocking the data potential in Digital Health. Enabling cross-organizational interoperability of Digital Health data, requires different actors in the healthcare ecosystem to mutually share data.

Figure 1: Digital health data and data sources

The Health Information and Management Systems Society (HIMSS) defines interoperability as “the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally“. HIMSS1 further differentiates between four levels of interoperability in terms of information exchange:

  1. Foundational
  2. Structural
  3. Semantic
  4. Organizational

The highest level (Organizational) provides ‘secure, seamless, and timely communication and use of data both within and between organizations, entities, and individuals'. Digital Health services need to share data and seamlessly communicate both on intra- and cross-organizational levels to enable full data operability.

Benefits Enabled by Interoperability

Information exchange between actors in the Digital Health ecosystem allows them to provide efficient digital health services. Some of the key interoperability benefits of Digital Health include:

  • Earlier diagnoses and patient empowerment: Patient Health Data could be processed by AI-based systems that could diagnose diseases more quickly and accurately than by humans and empower patients to participate in decision-making alongside their Healthcare Professional (HCP).

  • Care coordination: The availability of patient data with clinics, laboratories, imaging centers, and public health organizations could allow HCPs to make better informed decisions and coordinate care between stakeholders in the healthcare ecosystem more efficiently.

  • Improved therapies: Pharmaceutical companies and academic R&D organizations would have access to a broader pool of diseases data that could enable the discovery of new therapies and adapt existing treatments.

  • Public Health Management: Public health authorities would have timely access to real-time surveillance data that could allow better responses to disease outbreaks and further allow improved pharmacovigilance.

  • Improved patient experience: Patient-centric applications could integrate services from various healthcare providers such as payers, pharmacies, laboratories, researchers, and public health organizations and allow automatic exchange of patient data. Seamless data integration of involved healthcare actors would provide integrated view of patient health and thus improve patient experience throughout the entire value chain.

  • Improved administrative processes: The exchange of administrative and billing data between healthcare providers and payers could be streamlined with standardized interfaces between organizations. It would enable easier information retrieval, reduce paper burden, and thus reduce healthcare costs.
Interoperability Challenging Aspects in Digital Health

Lack of interoperability of Digital Health services would mean sub optimal collaboration between organizations. This situation would mean incomplete understanding of patient’s health, disjointed and inaccessible patient information, and fragmented patient experiences.

The critical challenging aspects (see Figure 2) that need to be addressed for reaching interoperability in the healthcare ecosystem can be categorized as follows:

  • intra-organizational challenging aspects
  • cross-organizational challenging aspects

Intra-organizational challenging aspects need to be addressed to enable operability within an organization, whereas cross-organizational challenging aspects require alignment among stakeholders belonging to different organizations.

Inter- and Cross organisation challenging aspects grafic
Figure 2: Interoperability challenging aspects
  • Data privacy: Patient consent to process their own data
    (Personal Health Information – PHI) forms the prerequisite for information free-flow between providers and enables interoperable services. Many patients are still skeptical about the use of their PHI for Digital Health applications and concerned about the potential misuse of data.

  • Cybersecurity: Unless the transfer and processing of PHI is sufficiently protected, the information exchange between providers could result in data theft or misuse. Ensuring patient data security in organizations and personal devices is a significant challenge for organizations and service providers.

  • Data alignment: Aligning on data exchange within the organization or between organizations requires agreeing on data interoperability standards and making data ready for exchange to allow semantic interoperability. Despite the adoption of standards such as FHIR and DICOM, its use has been inconsistent and slow. There are multiple standards in use for data transport, data content, data security, and disease terminology, which still need to become the de facto standards among digital health providers.

  • Data infrastructure: Many healthcare organizations have legacy systems in place that need to be updated or replaced in order to meet interoperability requirements. The technical infrastructure needs to have application interfaces to communicate with external organizations in a secure manner. The build-up of infrastructure could involve significant decisions related to digital health platform to manage data from digital devices.

  • Collaboration and commitment of healthcare ecosystem actors: The interoperability of Digital Health data between organizations requires guidelines from regulatory agencies and strong collaboration between healthcare organizations. Lack of interest and commitment by those organizations could affect the pace of interoperability implementation.
Looking Forward

The benefits of Digital Health data interoperability are significant. However, there remain challenges in realizing those benefits. Data interoperability will be a critical component in the shift towards a patient-centric approach that enables personalized care and improved outcomes. In part 2 of this blog series, we provide recommendations to address key challenges in establishing data interoperability in the life sciences and healthcare industry.

For further insights into patient-centric healthcare, we recommend checking out our article Patient-centric digital healthcare, where we delve deeper into it.  




1.    HIMSS (2024), Interoperability in Healthcare, (Accessed 14 March 2024)

Dr. Tom Philip

Life Sciences Consulting, Cognizant

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Dr. Tom Philip is a change and program management expert with a passion for enhancing customer experiences in the Digital Age. As a Life Science Consulting professional, he holds certifications in various areas, including Prosci Change Management Practitioner, PMP, MSP, SAFe RTE, and more.

David De Vidi

AI & Advanced Analytics, Cognizant

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David De Vidi enjoys working with his clients to conceptualise and design innovative data-driven initiatives that generate critical insights addressing business challenges and transforming business models and processes.During his 25 years’ long experience he has developed and led multiple engagements for 45+ companies at HQ, Regional and Affiliate levels in the areas of Business Advanced Analytics, Commercial Strategies & Operations, working both from within the industry and as a consultant.

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