• Interoperability
  • US ONC mission: to IOp and beyond!

    Buzz Lightyear, a Toy Story celebrity and star, saw his environment extending “To infinity, and beyond.” The US Office of the National Coordinator (ONC) sees interoperability (IOp) having an equivalent horizon. In an interview with EHRIntelligence, Genevieve Morris, ONC’s Principal Deputy National Coordinator, says the emergence of new technology and data sets means that IOP extends beyond a conventional EHR-to-EHR connectivity, which is becoming  an IOp  foundation.

    Extending IOp into genomic data and all other data needed for precision medicine’s ONC;s goal. They’re different data sets to mainstream healthcare data in EHRs. Learning health systems comprise totally integrated healthcare environments where IOp takes on a new and expanded meaning. This is a concept embedded in the ONCs’ updated healthcare IOp roadmap reported by eHNA. 

    While IOp and beyond may seem like a leap into the unknown, ONC’s approach’s pragmatic and incremental. It includes underlying standards and technical components needed for medical device integration and patient-generated health data. 

    Provenance’s an example. It identifies people and devices that create data elements and specifies when and where. This meets healthcare providers’ needs to know who and where data comes from as part of clinical data exchanged between EHRs.

    Africa’s IOp strategies and initiatives need to stretch out into these extra territories. It’s a continuous commitment to skills, resources and finance. EHRs are a start, not an end.

  • Oracle has a supply chain system for healthcare

    Effective logistics are essential for efficient and effective healthcare. Oracle aims to provide this by transforming healthcare’s supply chains. Its white paper from Fierce Markets set out the steps: 

    Better automation and analyticsTighter integration between all stakeholders, including end users, supply chain, distributors, suppliers and Group Purchasing Organisations(GPO)Increased emphasis on sound inventory management and demand planning. 

    Care and Cost Drive Healthcare Supply Chain Revolution says these can overcome four core challenges:

    Master data management, such as item pricing and trackingManual processes and multiple hand-offs between stakeholdersLegacy technologyA reactive instead of proactive approach.

    A general savings estimate, claimed as conservative is 3% to 5% of supply costs. These are achieved by enhanced strategic sourcing that can weed out supply chain redundancies. Actual savings depend on the levels of efficiency that healthcare providers have already achieved. Further gains may be from better re-order quantities and minimum and maximum ordering and stockholding levels. IoT solutions can help too.

    Oracle’s solution relies on a cloud service. For Africa’s health systems, it could be a big step forward. Better spending on drugs and medicines and avoiding counterfeits are high-value objectives. Improving their costs and availability has a direct impact on healthcare quality and efficiency. The Supply Chain Management System from Management Sciences for Health ( MSH) and operating across much of sub-Saharan Africa has insights in the impact.

  • What were the top ICT stories in 2017?

    Now 2017’s history, the significant ICT themes can be seen. A retrospective by Health IT Analytics found the top ten from its posts. They’re Big Data, Fast Healthcare Interoperability Resources ( FHIR) and machine learning are included. They’re:

    Top 10 Challenges of Big Data Analytics in HealthcareTop 4 Machine Learning Use Cases for Healthcare ProvidersWhat is the Role of Natural Language Processing in Healthcare?Judy Faulkner: Epic is Changing the Big Data, Interoperability GameHow Healthcare can Prep for Artificial Intelligence, Machine LearningExploring the Use of Blockchain for EHRs, Healthcare Big DataHow Big Data Analytics Companies Support Value-Based HealthcareBasics to Know About the Role of FHIR in InteroperabilityData Mining, Big Data Analytics in Healthcare: what’s the Difference?Turning Healthcare Big Data into Actionable Clinical Intelligence. 

    It’s a valuable checklist for Africa’s health informatics and ICT professionals for there personal development plans. eHealth leaders can use it too to ensure their eHealth strategies either include initiatives for the top ten, or lay down the investigative and business case processes for future plans. 

  • Patient ID architecture needs an overhaul

    As eHealth expands its reach across more health and healthcare activities, each health system needs a more reliable Master Patient Index (MPI). Three activities are limited without it: 

    Co-ordination across the healthcare continuum and locatonsAccessing patient informationResolving patient identities across disparate systems and enterprises. 

    These need patient ID architecture needs to switch away from episodic modes. A whitepaper from        

    Verato, a cloud-based platform that matches identities, sets out how. It’s based on three components:

    Agreed business rules and policies for sharing patient dataStandardised EMR access protocols andPatient identity matching. 

    Significant progress on Interoperability (IOp) for data sharing rules and Health Level Seven (HL7) provide a foundation. What’s needed now's a set of Unique Patient Identifiers (UPI) so data sharing unambiguously refers to each patient. Easy to say, and Verato acknowledges the logistical and politically constraints. 

    Using demographic identifiers, such as names, addresses, birthdates, genders, phone numbers, email addresses and social security numbers, to identify individuals and their EMRs are error-prone when captured at receptions. They change over time too. Between 8 and 12% of people have more than one identity across healthcare organisations. Their medical histories are spread randomly across these different IDs. These duplicates are one of healthcare’s most intractable challenges.

    Current MPIs were created in the late 1990s and broadly deployed over the last ten years. They use probabilistic matching algorithms that compare all demographic attributes to decide if there are enough similarities to make a match. Common changes, such as maiden names, old addresses, second home addresses, misspellings, default entries twins, junior and senior ambiguities, and hyphenated names aren’t detected. 

    Verato’s approach uses pre-populated, pre-mastered and continuously-updated demographic data

    spanning countries’ populations. It referential matching that leverages the pre-mastered database as an answer key to match and link identities. This isn’t enough in eHealth’s changing and expanding world.

    Verato also aims to deal with:

    Adding new ICT by using standard Application Programming Interfaces (API)Automating existing MPI technologies stewardship, discovering missed duplicates and validating identities at registrationSupporting EHR consolidation where connections MPIs can’t reconcile patients’ data in other EHRsSupport HIE. 

    For Africa’s eHealth, these are valuable steps forward. It emphasises the need for better civil registration too, a long-standing challenge.

  • KLASified IOp needs to progress

    A bit like an horizon, as eHealth Interoperability (IOp) takes a step forward, its horizon seems like two steps further away. KLAS, the eHealth analyst outfit, has published its Interoperability 2017 report of its Cornerstone Summit. First Look at Trending – Some Progress toward a Distant Horizon,” summarises the findings. It’s the third interoperability summit. The KLAS 2017 research provides the first year-on-year comparison measuring progress. There’s plenty left to do.

    KLAS research shows that shared patient data often fails to benefit patient care much. It’s an important insight for EHR business cases, and reveals the ubiquitous gap between eHealth’s potential and its probability in realising its benefits. An essential question to ask before driving ahead investing scarce resources is asking eHealth sponsors to estimate the percentage of patient encounters in which:

    Outside data informs healthcare delivery betterUsers have access to needed data from outside their organisations. 

    Most of the report deals with methodologies and questions about measuring IOp. They provide a wide range of detailed and precise themes that Africa’s eHealth programmes can use to specify and test their IOp components.

    Other issues are: 

    Should behavioural health and home medical equipment be incorporated in post–acute care interoperability?Pharmacies are key partners in post–acute care IOp, so need includingWhich IOp capabilities and synergies should or should not exist between post–acute care and hospital systems?Should hospitals’ Emergency Department (ED) systems query HIEs to identify if patients receive home health services, and can the home health records and their patient information be added to ED systems?

    Healthcare’s concerns and insights include:

    Securing national IOp inter-organisational trust of incoming data and its accuracyClarity on liability of outgoing data not being used securely or guarded How to co-ordinate between organisations sharing data, especially when different users  need different data?How can patients help bridge IOp?IOp gaps in healthcare transitions are a significant market oversight and need fixingHow should information blocking be defined and implemented?

    Africa’s eHealth programmes can extract invaluable insights from the KLAS report. I can help them extend the stride of the next step. Whether it takes them closer to the IOp horizon’s another matter.

     

  • How can Africa innovate with Unique Patient Identifiers?

    Unique Patient Identifiers (UPI) are both essential and demanding to achieve. They’re harder to use when data’s transferred and shared between organisations. An article from the American Health Information Management Association (AHIMA) proposes innovation with UPIs propriety to vendors and customers as part of the solution. For African health systems, it may improve the current position until national UPIs are in place.

    US provider organisations and payers are innovating with propriety UPIs. A common theme’s dealing with real time or batch queries held by third parties, such as credit agencies. These already have UPIs for their commercial activities. It suggests they offer value to health organisations because commercial entities frequently update and constantly maintain their data, providing current demographics for data warehouses, population health management and illness prevention.

    UPI innovation must be integrated with eHealth governance, which need developing in African health systems. Through eHealth governance, UPI innovation can engage with stakeholders such as:

    Governance teamsProfessional bodiesPatient access and registration staffHealth information management teamsICT teamsData users, such as care coordinators and health analytics teams.

    Their roles can extend to strategic information governance and how innovation and success will be applied. Mitigating risks is another role they can participate in.

    A set of generic questions can help to define UPI innovation:

    Who’s responsible for identifiers’ integrity, especially new identifier created by innovation?When existing data’s augmented with new external data, how is the new data integrated, and what is its lifecycle of managed?What are acceptable uses for the identifiers set by legal and regulatory requirements for UPIs, privacy and compliance?How can organisations incorporate UPI technology with human data stewardship to ensure a compliance and governance?How are discussions and findings from UPI innovation relayed to eHealth governance?How can discussions be for ICT, and people and process supporting eHealth governance?Should innovation deal with data creation for patient access or registration, data governance through procedures, processes and data fields standardisation, or both?How can a sample database be built to support proof of concept and technology?How can enough data be included in UPI innovation projects for rigorous, reliable testing, such as 100,000 records?How can UPI data goals be integrated into data governance programmes?

    AHIMA’s article says organisations and healthcare professionals are cautious in applying innovation to the long-standing UPI challenge. Mismatching records can have profound, adverse effects, so reluctance is reasonable. Despite these anxieties, innovation can still proceed, provided it’s based on a rigorous risk assessment, impact probability, costs and benefits.

    UPI innovation creates two activities for Africa’s health systems. One’s setting up their UPIs. The other is constant, managed innovation with UPIs.

  • IHE updates cardiology, IT infrastructure and radiology frameworks

    It is important that Africa’s health systems and informatics teams contribute to Integrating the Health Enterprise (IHE) updates. They are opportunities to help to shape eHealth’s essential building blocks and how they change.

    IHE has put out three framework updates:

    Cardiology Procedure Note (CPN) Rev. 1.1IT Infrastructure Technical Framework SupplementsRadiology volumes 1 to 4.

    The IHE Cardiology Technical Committee says trials began on 4 August 2017. They may be available for testing at IHE Connectathons. Comments on the changes can be submitted at any time.

    The IHE IT Infrastructure Technical Committee has published supplements for trial implementation, also from 4 August 2017. These profiles may be tested at IHE Connectathons and comments are invited at any time. They deal with:

    Mobile Care Services Discovery (mCSD) Rev. 1.1Mobile Cross-Enterprise Document Data Element Extraction (mXDE) Rev. 1.1Non-patient File Sharing (NPFSm) Rev. 1.1.

    Four updated Radiology Technical Framework (RADTF) volumes deal with:

    Volume 1 (RAD TF-1) Integration ProfilesVolume 2 (RAD TF-2) TransactionsVolume 3 (RAD TF-3) Transactions (continued)Volume 4 (RAD TF-4) National Extensions.

    Like the cardiology and IT infrastructure updates, comments can be submitted at any time and profiles may be tested at subsequent IHE Connectathons.

  • Kenya’s mHealth standards set out governance and policy rules

    Leadership’s seen as an underpinning component of mHealth governance and policy. Kenya Standards and Guidelines for mHealth Systems sets out the Ministry of Health approach to framework of strategies, plans, budgets, governance and policy.

    Kenya already has a governance framework. It integrates three stakeholder types, policy, suppliers and users. It fits into its institutional governance framework described in Kenya National eHealth Policy 2016 to 2030. Its mHealth governance arrangements fit within its three main policy stakeholder parts of policy, suppliers and users. Each one sets out stakeholders’ roles and responsibilities.

    Its regulation standards extend across:

    A certification frameworkProtection of privacy and confidentialityManaging disclosures of health informationSource code and application ownership.

    Governance has four main parts:

    SecurityValidationAccountabilityOwnership.

    These are huge steps forward for all Africa’s eHealth. A possible trajectory for eHealth governance may be towards the standards released by the American Health Information Management Association (AHIMA). An eHNA post summarised these. COBIT 5 is an international for ICT governance in all economic sectors. Published by ISACA, It’s been adopted by AeHIN. As an extremely sophisticated governance model, it shows a possible destination of Africa’s eHealth governance.

  • Pocket mHealth's patient-centric and advances IOp

    Combining the synergy of patients, their mobiles and healthcare’s a growing ambition. Pocket mHealth likes the idea. It’s an app that brings EHRs to smartphones. The group is part of Atos Research & Innovation based in Atos Spain. It can fit Africa’s programmes for mHealth and EHRs.

    Validated by medical professionals, Pocket mHealth aims drives the paradigm shift needed for person-centric medical care. It provides access to EHRs so users can improve the way they take care of their health. An emphasis on Interoperability (IOp) and eHealth standards enabling integration of clinical data from heterogeneous Hospital Information Systems (HIS), it supports benefits such as better clinical efficiency, fewer medical errors and lower costs.

    Pocket mHealth’s underlying philosophies are:

    Clinical data belongs to appropriate citizensUsers supervised by corresponding, responsible health professionals.

    These are achieved by Pocket mHealth’s validation by medical professionals. Other features include:

    Improved diagnosesSuppressing unneeded paper or DVD reportsAvoiding duplicate and redundant testsEHRs are continuously updated and complete, enabling better health and quality of life decisionsSupporting patient mobility with accessible clinical data that enables better healthcare in rural or holidays locationsCyber-security mechanisms that guarantee the privacy and data security.

    Both the vision and type of solution fit Africa’s needs. Its strategies and programmes for EHRs can incorporate secure IOp links to citizens’ smartphones. 

  • Kenya’s mHealth standards are strong on IOp

    Kenya’s Ministry of Health has set a solid foundation for its next step in eHealth regulation and good practices. The second main section in Kenya Standards and Guidelines for mHealth Systems deals with information exchange and Interoperability (IOp). It has a seven stage model of IOp maturity, including level 0 for no maturity and three conventional IOp classifications of technical, syntactic and semantic. They’re:

    Conceptual, enabling other engineers to understand documentation and evaluationDynamic, to recognise and comprehend data changes in systems over timePragmatic, including modest AISemanticSyntactic and workflow integrationTechnical and integratedNone, so can be ignored.

    They combine into three categories, integration, IOp and composability for maximum interoperation. It’s a requirement that all Kenya’s mHealth complies with its IOp standards. These include Health Level (HL)7 version 3 for clinical messaging and International Classification of Diseases (ICD) 10, Systematized Nomenclature of Medicine (SNOMED) for coding, Logical Observation Identifiers Names and Codes (LOINC) and Rx Norm for pharmacies.

    Developers have to provide Standards for Applications Programming Interfaces (API) to define how their mHealth interacts with other systems. It fits into a Fast Health Interoperability Resources (FHIR) architecture. It complies with Integrating the Healthcare Enterprise (IHE) and HL7 standards

    While these apply to health and healthcare data, Kenya’s standards apply to social health determinants too. It’s an indicator of the breadth of its approach.