Data Interoperability: Addressing the Challenges Placing Quality Healthcare at Risk

The data interoperability challenge in global digital health, as experienced in the context of the variety of national electronic health record (EHR) deployments around the world, should have been avoidable. This is not to say that EHR deployments and data integration are ever easy, but some of the standards and architectural principles designed to minimize the scale of the challenge have been available to the IT and data communities for decades. Whether due to ignorance or by choice, not applying these principles has come at a heavy price for many countries, leading to expensive EHR and interoperability failures.

While more than 50% of EHR deployments have failed, what is worse is that poor EHR deployments resulted in 18,000 EHR-related patient safety issues between 2007 and 2018 in the United States,¹ an annually growing statistic that may be “drastically underreported.”² A major driver of the issue is the presence of errors in patient medical histories, which result in incorrect diagnoses being made. Of the 21% of US patients who were aware enough to identify errors in their EHR data, the worst type of error they identified was incorrect medical history.³ Quality healthcare is high stakes; these statistics should concern patients, healthcare providers, and data practitioners.

A standards- and architecture-based approach should be used to minimize digital health interoperability risk in EHR deployments to reduce the negative health impact on patients.

EHR and Interoperability

The aim of an EHR system is to enable a single, complete health record and medical history of each patient. This depends on data interoperability, often facilitated by means of a health information exchange (HIE) that provides an integrated view of the patient's medical history across health systems in the EHR, allowing healthcare providers to make more accurate diagnoses and provide personalized care.

Figure 1 shows an example of a health practitioner accessing a patient's dermatological records through an EHR via an HIE. Many systems (e.g., a cardiologist's system) connect to an HIE in this manner. HIEs take on various forms: They can be centralized, federated, hybrid, private, or decentralized. Figure 1 is an example of a centralized HIE architecture.

Data interoperability is critical to effective national healthcare because it facilitates at least six healthcare advantages:

1. Patient-centric care–Enabling the seamless exchange of health information across different healthcare systems so that patients can benefit from coordinated and informed healthcare decisions
2. Continuity of care–For patients who are treated by multiple healthcare providers, such as a primary care doctor, a specialist, a hospital, or a home care agency
   
   

   Data governance practices should be established to enable the effective management and curation of data through policies and procedures for data quality and data integration, including definitions of data roles and responsibilities.

3. Data accuracy and quality–From both a reference data and a master data perspective
4. Operational efficiency–Due to reduced errors and duplications
5. Research and public health activities–by means of aggregated data sources
6. Patient engagement–Enabling patients to access and interact with their own health data to proactively manage their health needs

Effective data interoperability depends on an approach based on various technical, organizational, and policy considerations. Organizations must adopt standardized data formats, protocols, and terminologies to ensure that health information can be seamlessly exchanged between different systems. Data governance practices should be established to enable the effective management and curation of data through policies and procedures for data quality and data integration, including definitions of data roles and responsibilities. Indeed, EHR disruptions have been blamed on a lack of standards and operational processes, as in Ontario's CA$1 billion eHealth scandal of 2009.⁴

Stakeholders, Standards, and Architecture

It is worth considering the role of stakeholders, standards, and architectures in enabling interoperability.

Stakeholders
A major challenge for data interoperability is the volume of stakeholders involved. Typical stakeholders include patients, IT vendors, healthcare providers, payers (insurers), regulators, healthcare organizations, public health agencies, information exchanges, research institutions, standards development organizations, quality and accreditation bodies, pharmaceutical companies, privacy and security experts, and even patient advocacy groups. Coordinating these stakeholders can take considerable management effort.

Operational Standards
Then there is the challenge of getting stakeholders to agree to operate according to a set of global standards to ensure national and international health operations consistency. International consistency is critical for the cross-border management of global events such as the COVID-19 pandemic.

Various standards and frameworks are available to support EHR deployments, data interoperability, and allied activities, some of which are:

• COBIT⁵
• Data Management Association (DAMA) Data Management Body of Knowledge (DMBOK)
• Information Technology Infrastructure Library (ITIL)
• International Organization for Standardization (ISO)/Technical Committee 215 Health Informatics
• ISO 8000 Data Quality
• ISO/International Electrotechnical Commission (IEC) 8183 Information Technology–Artificial Intelligence–Data Life Cycle Framework
• ISO 9000 Quality Management Systems
• ISO 9241 Ergonomics of Human-System Interaction
• ISO 13606 Health Informatics–Electronic Health Record Communication
• ISO 14971 Medical Devices–Application of Risk Management to Medical Devices
• ISO 20000 Information Technology–Service Management
• ISO 22301 Security and Resilience–Business ContinuityManagementSystems
• ISO/IEC 27001 Information Security ManagementSystems
• ISO/IEC 27017 Security for Cloud Services
• ISO 31000 Risk Management Standard
• ISO/IEC 38500 Information Technology– Governance of IT for the Organization
• US National Institute of Standards and Technology (NIST) Cybersecurity Framework
• The Open Group Architecture Framework (TOGAF)

These frameworks and standards collectively contribute to the strategic planning, enterprise architecture, standardization, interoperability, IT governance, change management, data management, integration, security, privacy, compliance, and vendor management for EHRs and data integration.

Reference Data Standards
Several reference data standards have been developed that are maintained by various organizations and bodies to facilitate the seamless exchange of health information and encourage the adoption of best practices in healthcare technology. Reference data is essentially the set of lookup codes in a data ecosystem. For example, the RxNorm code for the ciprofloxacin 500 mg 24-hour extended-release tablet is RX10359383.

Examples of health reference data standards are:

• RxNorm RxNorm describes normalized names for clinical drugs.
• Health Level Seven (HL7) A widely recognized set of standards, HL7 enables the exchange, integration, sharing, and retrieval of electronic health information.
  • Clinical Document Architecture (CDA) An HL7 standard that outlines the structure and semantics of clinical documents, such as discharge summaries and progress notes, CDA is employed to share structured clinical information in EHRs.
  • Consolidated Clinical Document Architecture (CCDA) An extension of CDA, CCDA prescribes a common set of clinical document types to enhance interoperability betweenEHR systems.
  • Fast Healthcare Interoperability Resources (FHIR) A modern HL7 standard for the electronic exchange of healthcare information, FHIR is designed to be lightweight and easily implementable, making it ideal for modern healthcare applications.
• Digital Imaging and Communications in Medicine (DICOM) A standard for the management, storage and exchange of medical images and related information, DICOM is commonly employed in radiology and medical imaging applications.
• International Classification of Diseases (ICD) A global standard for the classification of diseases and health conditions, ICD is crucial for coding diagnoses and reasons for healthcare visits.
• Logical Observation Identifiers Names and Codes (LOINC) A standard for identifying laboratory and clinical observations, such as blood tests and vital signs, LOINC ensures consistent and accurate data exchange.
• Cross-Enterprise Document Sharing (XDS) A profile within the Integrating the Healthcare Enterprise (IHE) framework, XDS defines standards for sharing clinical documents across healthcare enterprises, including hospitals and clinics.

Master Data Resolution Processes
Patients, health providers, health insurers, and healthcare facilities are all examples of master data entities. The problem of different data being stored for the same patients across the various systems within the digital health ecosystem describes an entity resolution class of data problems for patient data. Entity resolution is a critical part of digital health, and various activities can help resolve it (figure 2).

Record linkage (or data matching) processes are used to identify and merge patient records that have an assessed probability of representing the same patient across different systems. Although there is no standard for entity resolution, the primary elements of a high-precision entity resolution process are:

• Data matching algorithms-Other than raw text mapping, algorithms such as Jaccard similarity, Levenshtein distance, or Soundex can be used.
• Feature selection-Determining the best combinations of data quality, uniqueness and discriminatory power is key to identifying the features that will be matched.
• Blocking and clustering-The criteria for blocking or clustering must be determined.
• Scalability-The scale of entity resolution processes to handle large datasets efficiently can be managed by considering parallel processing and distributed computing.
• Thresholds and decision rules-Threshold values and decision rules to determine when two records should be considered a match must be specified.
• Probabilistic matching-Probabilistic matching techniques that provide a measure of the likelihood that two records represent the same entity should beincorporated.
• Handling missing or incomplete data-An approach for handling missing or incomplete data, such as imputation or the use of confidence scores, must be defined.
• Confidence and uncertainty-Confidence scores to match decisions to quantify the uncertainty associated with each match must be assigned.
• Data privacy and security-Entity resolution processes should adhere to relevant data protection laws and regulations.
• Evaluation metrics-Evaluation metrics, such as precision and recall, should be specified to assess the incidence of false positives and false negatives and the overall performance of the entity resolution methods.

One approach to patient entity resolution has been to perform these elements in an HIE by means

of a master patient index (MPI),⁶ an activity that has existed since the 1950s.⁷ The benefit of a national HIE is that the approach can serve national health interests rather than solely regional or even institution-level interests. An instrument such as an MPI is critical to effective health analytics.

Data quality is a critical success factor for entity resolution. One cannot perform quality matching if the entity data is dirty. In other words, successful entity resolution depends on good data quality.

Enterprise Architecture and Data Architecture
Enterprise architecture, as reflected by frameworks such as TOGAF and Zachman, has its roots in the 1960s and came into its own in the 1980s.⁸ Data architecture—rooted in the work of Edgar Codd (1970) and Peter Chen (1976)—required the elimination of at least two assumptions:⁹

1. That each computer program should be isolated from other programs-Elimination is desirable because this paradigm resulted in duplication. Codd's contribution uncoupled data's layout from its storage.
2. That input and output are equal-Elimination is desirable because this paradigm resulted in the activities of data creation being seen as requiring the same effort and skills as data consumption, whereas they can vary tremendously. Chen's contribution highlighted the differences between data creators and data consumers.

The core tools for an architected approach to data have been available for at least 40 years. Yet data interoperability makes up the top objectives of today's digital health strategies¹⁰ and is a major problem in healthcare (think about the physical movement of paper files between doctors when a patient changes doctors or as a result of a referral). Good data practices do not seem to be mainstream in healthcare yet. In the United States, “as much as 75% of all medical communications are done through fax machines. … The fax machine has remained supreme because attempts at interoperability have not reached the universal adoption that faxes provide.”¹¹

An important characteristic of interoperability is that the systems in the healthcare environment can communicate with each other. TOGAF provides an example of an architectural principle that must come into play when selecting technology—Principle 21: Interoperability. The principle statement is, “Software and hardware should conform to defined standards that promote interoperability for data, applications, and technology,” and the rationale for the statement is, “Standards for interoperability additionally help ensure support from multiple vendors for their products, and facilitate supply chain integration.”¹² This has implications for the selection of IT platforms in the health ecosystem, a critical activity that may be ignored, especially in donor-funded healthcare scenarios that only fund one system at a time, thereby perpetuating siloed systems development.

The Data Life Cycle

Today, data architecture is concerned with the infrastructure that enables the data life cycle. Data life cycle management constitutes domain 3 of ISACA's Certified Data Privacy Solutions Engineer (CDPSE)® certification, in this case ensuring that privacy activities are part of each phase of the data life cycle by design. The stages of the data life cycle (figure 3) include:¹³

• Data creation (not only data capture, but data sourcing in general)
• Data storage
• Data use
• Data transmission
• Data sharing
• Data destruction (or archiving)

Data life cycle management is an important paradigm because it ensures data is managed according to differences in data management and data governance requirements across phases in support of an organization's data-driven decision-making and risk mitigation practices.

Given the volume of sensitive data that is available on EHRs, including genetic information, test results, and even surgery data, the focus on security and privacy should be paramount.

When EHRs and IoT Devices Do Not Speak the Same Language

Internet of Things (IoT) devices play a major role in healthcare such as by facilitating remote monitoring and care for patients who are unable to travel or have difficulty traveling. A requirement for effective remote monitoring is a secure architecture that enables data from IoT devices to integrate with EHR data for monitoring and analysis.

EHRs and IoT devices are unable to speak the same language not only when their structures and formats differ, but also when their metadata differs. Even fields with identical structures, formats and naming conventions may mean different things. This distinction is often missed even by the most well-intentioned data teams. Much of the same language problem is resolved by the reference data standards and master data processes, but the importance of quality metadata cannot be overstated in efforts to harmonize those languages.

The Tragedy of Privacy and Security in EHRs

One cannot discuss interoperability without referring to the data privacy and security implications of integrated master and transactional data. Although global EHR adoption is constrained by privacy and security concerns (as well as by interoperability, cost, training, and change management concerns),¹⁴ public hospitals in Malaysia have found the opposite: that good privacy and security practices have a positive impact on EHR adoption rates.¹⁵ In other words, demonstrably good privacy and security have the potential to positively impact and perhaps even accelerate EHR adoption.

Patient privacy risk emerges because of improper access to data, lax security practices, and a failure to audit access to EHRs. Given the volume of sensitive data that is available on EHRs, including genetic information, test results, and even surgery data, the focus on security and privacy should be paramount. To facilitate cybersecurity practices, standards such as ISO/IEC 27000 have been available for decades (starting as ISO/IEC17799 in 2000).¹⁷ Furthermore, free and high-quality cybersecurity and ransomware guidance is often provided on national government sites (e.g., by the government of Canada).¹⁸

Yet five Ontario, Canada hospitals, along with their shared IT provider, were recently the victims of a ransomware attack in which hospital operations information and patient, employee, and professional staff data was at stake.¹⁹ That attack was one of multiple recent security attacks on Ontario hospitals.²⁰ Although it is not yet clear what happened, indications that a shared IT provider was involved have renewed calls for performing cyber due diligence on IT vendors, a basic element of good cybersecurity practice.

The Tragedy
There is hope that healthcare organizations will take cybersecurity seriously, demonstrated by an appropriate allocation of resources—both monetary and in terms of skills and systems—to the protection of patient data. However, the tragedy of the suboptimal allocation of resources to cybersecurity is always felt by the patients, who are often left to their own devices to resolve problems that result, despite any platitudes that may be communicated post-event by the leadership of the compromised organization in its press releases.

The Risk of Vendor Lock-In

Vendor lock-in in EHRs occurs when a healthcare provider or organization becomes dependent on the vendor for access to the system's data. Vendor lock-in poses significant risk to data interoperability, as it limits the ability to share and exchange patient information with other healthcare providers and systems.

The risk of EHR system vendor lock-in must be determined by conducting a thorough vendor evaluation and gaining a detailed understanding of the contract before signing it. Open standards and interoperability must be embraced at the outset, allowing for flexibility and improved patient care.

Data Governance: In the Eye of the Beholder

The term data governance has been more readily used throughout the last five to eight years than in the preceding 20 years. The challenge with the term is that many still confuse data governance with data management.²¹

In brief, data governance concerns the oversight of data management activities (e.g., privacy, security, data quality, metadata, master data) aligned with defined policies, process, roles, and responsibilities with respect to the role of data in the context of an organization's data goals.²² Figure 4 presents the data management activities that should be subject to data governance.

Data governance activities should be specific to each phase of the data life cycle to ensure privacy, security, quality, and compliance for critical and sensitive data.

While data governance is applicable to interoperability in at least two ways (semantically and structurally in figure 5 and reflected in figure 4),interoperability also requires IT governance and enterprise governance (foundationally and organizationally in figure 5) to be most effective.²³

Foundational interoperability concerns the requirements for systems to be able to communicate with each other—a key IT architecture principle. Foundational interoperability is a critical success factor for data interoperability, so IT governance must ensure both the creation of IT architectural principles in the health ecosystem and compliance with them. In addition, enterprise governance encapsulates all levels of organizational governance (apart from corporate governance), ensuring policy and legal alignment to enable effective interoperability. The policy conversation may extend to the provincial or national level.

What Lies Ahead

Digital health trends in edge computing, AI, blockchain, and IoT (e.g., wearables) are enabled by additional pressing patterns in interoperability that have not been adequately resolved:

• Regarding the roles of privacy and security in facilitating adoption for healthcare providers, a 2021 study found that 30% of respondents would be more likely to use digital health technologies if they had more confidence in the providers' privacy and security.²⁴ Privacy and security are growing concerns.²⁵
• The use of application programming interfaces (APIs), as shown in figure 1, results in simpler data exchange.²⁶
• The cloud continues to offer benefits of scalability and flexibility. Further, a growing portfolio of tools is being made available by the major cloud vendors to facilitate data processing and analysis.
• Given the growth expected of various types of health data becoming available and accessible to an increasing number of healthcare actors, data governance is critical to ensure the privacy and security of patient data.²⁷
• Although the current focus is on the mechanics of interoperability, there is a growing effort to determine how to process and optimize data, suggesting the need for interoperability governance consisting of policies, resources, education, and technology solutions.²⁸

Although each of these trends is beneficial for digital health and for interoperability, they each have operational and risk considerations. One operational consideration is establishing a data governance organizational structure if one does not exist. A risk is that some cloud migrations can make organizations heavily dependent on their cloud service provider,²⁹ given the lack of portability and interoperability between cloud platforms at different service levels,³⁰ raising the spectre of vendor lock-in. Another risk stems from APIs. An organization's security operating model must be modernized to protect against the materialization of API-based security risk (e.g., mitigating the OWASP list of API security risk factors).

The topic of digital trust has not yet entered the act, and that is because health data sharing “only moves at the speed of trust, and right now it's slow going.”³¹ Incidents such as data breaches do little to increase trust in digital health. Building and maintaining trust in digital health necessitates robust cybersecurity measures, strict compliance with data privacy regulations (not only legally, but also ethically), and ongoing efforts to educate and engage both patients and healthcare providers in the responsible and secure use of digital health technologies.

There is some concern on the trust front, as hospitals—oncethemosttrustedinstitutions—dropped from 89% trusted in 2019 to 83% in 2020.³² This was still significantly higher than government, at 38% in 2020, but the decreasing trend negatively impacts the consumer adoption of digital health technologies.

Toward Successful Interoperability

There are several major interoperability challenges in the context of EHRs and digital health. A significant driver of success is to leverage the considerable weight of global architectural frameworks and standards. These alone will help with interoperability challenges such as lack of knowledge, data inconsistency and duplication, systems and service management, management of large data volumes, and human error that can reduce the serious and potentially life-threatening acts and ensure that the reduction is sustainable under good data governance. This holds true for both the integration of human-captured data and for machine (IoT)-captured data.

With respect to the challenges of adoption of EHR and digital health technologies by healthcare professionals and patients, an appropriate resource-backed plan to address privacy compliance (and data ethics) as well as data security is key to increasing adoption for both sets of actors, the very reason for pursuing data interoperability. Privacy and security are also the keys to trust. Because the operating nature of some vendors may hinder the achievement of full interoperability, the dream of a single health record for a single patient is currently impossible. So, vendor due diligence should be navigated with care.

Data governance is the glue that holds this all together and can be instrumental in growing trust if it is performed transparently. While data governance concerns the structures, policies, and processes aimed at achieving effective health data interoperability, governance activities are not spread equally across the health data life cycle. Having only part of the data life cycle well governed is almost as bad as having no governance at all. The risk that will seep through the ungoverned cracks could be the making of a national tragedy.

Overall, the goal is to ensure that negative digital health outcomes—such as the EHR-related patient safety issues—are minimized or eliminated altogether.

Endnotes

¹ Green, J.; “10 EHR Failure Statistics: Why You Need to Get It Right First Time,” EHR in Practice, 3 January 2020, http://www.ehrinpractice.com/ehr-failure-statistics.html
² Schulte, F.; E. Fry; “Death By 1,000 Clicks: Where Electronic Health Records Went Wrong,” KFF Health News, 18 March 2019, http://kffhealthnews.org/news/death-by-a-thousand-clicks/
³ Op cit Green
⁴ Webb, D.; “eHealth Ontario: $1B Later, the Apps Aren't Done,” ITWorld Canada, 7 October 2009, http://www.itworldcanada.com/article/ehealth-ontario-1b-later-the-apps-arent.done/39888"
⁵ ISACA, “COBIT,” http://ub5s.jayconscious.com/resources/cobit
⁶ Knudson, J.; “Identifying Patients in HIEs,” For the Record, vol. 24, iss. 8, 23 April 2012, http://www.fortherecordmag.com/archives/042312p10.shtml
⁷ Jayatisa, W.; V. Dissanayake; R. Hewapathirane; “Review on Master Patient Index,” Dental Research, vol. 1, iss. 1, 2018, http://arxiv.org/ftp/arxiv/papers/1803/1803.05994.pdf
⁸ Ardoq, “The Evolution of Enterprise Architecture,” 13 December 2022, http://www.ardoq.com/blog/evolution-of-enterprise-architecture
⁹ Foote, K.; “A Brief History of Data Architecture: Shifting Paradigms,” Dataversity, 3 February 2022, http://www.dataversity.net/brief-history-data-architecture-shifting-paradigms/
¹⁰ World Health Organization, “National eHealth Strategy Toolkit,” 2012, http://www.who.int/publications/i/item/national-ehealth-strategy-toolkit
¹¹ May, T.; “The Graveyard of InteroperabilityInitiatives in the Past & How We Can Drive the Future,” Medium, 18 April 2022, http://travismay.medium.com/the-graveyard-of-interoperability-initiatives-in-the-past-how-we-can-drive-the-future-5ff3ec1f1a12
¹² The Open Group Architecture Framework (TOGAF), “Architecture Principles,” The TOGAF Standard, Version 9.2, http://pubs.opengroup.org/architecture/togaf9-doc/arch/chap20.html
¹³ International Organization for Standardization/International Electrotechnical Commission (ISO/IEC), ISO/IEC JTC 1/SC 27 N 22088, ISO/IEC PWI 27045 Information Technology—Big Data Security and Privacy—Guidelines for Data Security Management Framework, The British Standards Institution, http://standardsdevelopmentbsigroup.com/projects/9021-06461#/section
¹⁴ Frackiewicz, M.; “The Global Adoption of Electronic Health Records: A Look at EHRs Around the World,” TS2, 13 July 2023, http://ts2.space/en/the-global-adoption-of-electronic-health-records-a-look-at-ehrs-around-the-world-2/
¹⁵ Enaizan, O.; B. Eneizan; M. Almaaitah; et al.; “Effects of Privacy and Security on the Acceptance and Usage of EMR: The Mediating Role of Trust on the Basis of Multiple Perspectives,” Informatics in Medicine Unlocked, vol. 21, 2020, http://www.sciencedirect.com/science/article/pii/S2352914820306006
¹⁶ Boyles, O.; “What You Need to Know About EHRs and Patient Privacy,” ICANotes, 6 June 2019, http://www.icanotes.com/2019/06/06/what-you.need-to-know-about-ehrs-and-patient-privacy/
¹⁷ Vanderburg, E.; “Information Security Compliance:ISO 27000,” TCDI, 7 December 2011, http://www.tcdi.com/iso-27000-certification.history-overview/
¹⁸ Canadian Centre for Cyber Security, “Baseline Cyber Security Controls for Small and Medium Organizations,” Government of Canada, February 2022, ; Canadian Centre for Cyber Security, “Ransomware,” Government of Canada, December 2021, http://www.cyber.gc.ca/en/guidance/ransomware
¹⁹ The Canadian Press, “Ontario Hospitals Say Data Has Been Published Following Ransomware Attack,” Global News, 2 November 2023,http://globalnews.ca/news/10067601/ontario-hospitals-data-published-ransomwareattack/amp/; Freeman, J.; “Five Ontario Hospitals Say Data Stolen in Cyberattack Has Been Published Online,” CP24, 2 November 2023, http://www.cp24.com/news/five-ontariohospitals-say-data-stolen-in-cyberattack-has-beenpublished-online-1.6628587
²⁰ Ibid.
²¹ UPP Global Technology JSC, “Data Governance vs. Data Management: Understanding the Difference,” LinkedIn, 28 July 2023, http://www.linkedin.com/pulse/data-governance-vs-management-understanding-difference/; Freeman, J.; “Five Ontario Hospitals Say Data Stolen in Cyberattack Has Been Published Online,” CP24, 2 November 2023, http://www.cp24.com/news/five-ontario-hospitals-say-data-stolen-in.cyberattack-has-been-published-online-1.6628587
²² Ganjhu, P.; “The DAMA-DMBOK Functional Framework: A Comprehensive Approach to Effective Data Management,” Medium, 24 May 2023, http://pawankg.medium.com/the-dama.dmbok-functional-framework-a-comprehensive-approach-to-effective-data-management-3de06af66cf2; Op cit Foote; Olavsrud, T.; “What Is Data Governance? Best Practices for Managing Data Assets,” CIO, 24 March 2023,
²³  Healthcare Information and Management Systems Society (HIMSS), “Interoperability in Healthcare,” http://www.himss.org/resources/interoperability-healthcare
²⁴ JABIL, “Top 8 Digital Health Trends in Technology,” 2021, http://www.jabil.com/blog/connected-health-technology-trends.html
²⁵ Das, M.; “Healthcare Interoperability Solutions: New Trends Solving Age-old Challenges,” Nalashaa Healthcare Solutions, 29 December 2022, http://blog.nalashaahealth.com/healthcare-interoperability-solutions-trends/
²⁶ ClinDCast, “The Future of EHR Interoperability: Advancements, Initiatives, and Impacts,” LinkedIn, February 2023, http://www.linkedin.com/pulse/future-ehr-interoperability-advancements-initiatives-impacts/; Op cit Das
²⁷  Stearns, M; S. Clark; “Preparing for the Rising Tide of Interoperability in Healthcare,” Journal of Ahima, 15 August 2022, http://journal.ahima.org/page/preparing-for-the-rising-tide-of-interoperability-in-healthcare
²⁸ Ibid.
²⁹ Shilawat, S.; “Cloud Interoperability and Portability,” Forbes, 22 June 2018, http://www.forbes.com/sites/forbestechcouncil/2018/06/22/cloud-interoperability-and-portability/?sh=133022384577
³⁰ Di Martino, B.; G. Cretella; A. Esposito; “Cloud Portability and Interoperability,” Springer Cham, Germany, 18 March 2015, http://link.springer.com/book/10.1007/978-3-319-13701-8
³¹ Knowles, M.; A. Krasniansky; A. Nagappan; “Consumer Adoption of Digital Health in 2022: Moving at the Speed of Trust,” Rock Health, February 2023, http://rockhealth.com/insights/consumer-adoption-of-digital-health-in.2022-moving-at-the-speed-of-trust/
³² Safavi, K.; B. Kalis; “How Can Leaders Make Recent Digital Health Gains Last?” Accenture, 26 August 2020, http://www.accenture.com/ca-en/insights/health/leaders-make-recent-digital.health-gains-last

GUY PEARCE | CGEIT, CDPSE

Has an academic background in computer science and commerce and has served in strategic leadership, IT governance, and enterprise governance capacities. He has been active in digital transformation since 1999, focusing on the people and process integration of emerging technology into organizations to ensure effective adoption. Pearce maintains a deep interest in data and its disciplines that accelerated with the launch of his high school data start-up many years ago. He was awarded the 2019 ISACA^® Michael Cangemi Best Book/Author award for contributions to IT governance, and he consults in digital transformation, data, and IT.