EHR (107)

Like eHealth, there’s more than one definition of EHRs. Consequently, when it comes to procurement, it’s important to be specific about your definition and requirements. Dr Chrono has provided a checklist that can help Africa’s health systems with their eHealth strategies, plans and procure their EHRs. It has twelve components:

  1. Intelligent time-saving charting tools for operational efficiency, such as customisable medical templates, medical speech-to-text, dynamic photo charting and macros
  2. Customisation and flexibility, to tailor EHRs to practices and specialties
  3. Fully functional on mobile devices
  4. Integrated with laboratories so test  and imaging requests, provide referrals and send prescriptions are seamless, minimise paperwork and streamline administrative tasks
  5. Real time eligibility verification and billing
  6. Patient portal that’s user-friendly
  7. Flexible and simple patient admissions and check-ins
  8. Sharable patient educational material
  9. Available training and support for EHRs
  10. Regulatory compliance
  11. Data flexibility and portability
  12. Application Programming Interface (API) and third party integrations.

For Africa’s health systems, sustainable affordability’s a vital matter. Other sustainability requirements, such as connectivity, are essential too. With all these in place, they can concentrate on mitigating investment risks and benefits realisation. There’s always more work to follow on with eHealth.

Like the term eHealth, EHRs are not strictly and unambiguously defined. A study in the Journal of Medical Internet Research (JMIR) has researched the literature and set about the task. It also identified concerns and challenges. The findings are essential for Africa’s health systems as they move their EHRs on.

Its Personal Health Record (PHR) taxonomy comprises three main categories:

  1. Structures, the main data types and standards used
  • Data types in PHRs
  • Standards that PHRs can adhere to
  1. Functions that depicts the main goals and features of PHRs
  • Users profiles and types that interact
  • Interactions of patient types with PHRs
  • Data sources and techniques for information input
  • Goals that represents PHRs’ aims
  1. Architectures types and scope
  • Descriptions of the main architecture models
  • Coverage as physical locations and divisions for data

There’s a wide range of challenges and concerns that need addressing. There are four main categories:

  1. Collaboration and communication
  • Context-aware computing
  • Wearable computing and IoT
  • Artificial Intelligence (AI) for health
  • Personalisation, usability, familiarity and comfort
  • Managing medications
  • Data generated by patients
  1. Privacy, security and trust
  • Confidentiality and integrity
  • Data repository ownership
  • Authorisation and access control technologies
  • Secure transport protocols
  1. Infrastructure
  • Portability between devices, equipment and hardware
  • Efficiency and scalability
  1. Integration
  • Patterns in collecting medical data
  • Terminology
  • Interoperability.

For Africa’s health systems, these range from long-standing eHealth challenges to new challenges coming with constant eHealth innovations. They’re demanding to deal with, and increase with complexity the longer they’re left.

Three common requirements to progress are affordability, benefits realisation and health systems human eHealth capacity and capabilities. They need adding to the list.

At the core of eHealth sits EHRs. The WHO Global Survey 2015 and Capter 5 of the  WHO and Global Observatory for eHealth (GOe) publication eHealth Report of the third global survey on eHealth Global diffusion of eHealth: Making universal health coverage achievable, say there’s been steady growth in adopting national EHRs over the last 15 years. It’s’ jumped by 46% in the last five years. Africa has much more to do.

More than 50% of upper middle and high-income countries have adopted national EHRs. The rate in poorer countries is 15% and 35%. Africa’s average’s at the lower end.

EHRs depend on other eHealth for much of their data. Most, over 70%, national EHRs integrate with laboratory and pharmacy information systems. About 56% integrate with Picture Archiving and Communications Systems (PACS). African countries trail the global average on these too. Their investment’s about a third of the global average. Catching up on EHRs needs investment in these systems too, so a considerable resource, affordability requirement and undertaking.

WHO identified lack of funding, infrastructure, capacity and legal frameworks as investment barriers. eHNA has posted on numerous others. They’re mainly parallel investments needed to maximise benefits. Examples are cyber-security, ID management, an example in a recent post, and ferreting out and quelling undesirable “digital dystopia” of ineffective EHRs that doesn’t improve health, healthcare or make it more efficient, posted on the snake oil speech at the American Medical Association.

Africa’s need for more investment in EHRs and related eHealth and overcoming the barriers points to the important role of rigorous eHealth business cases. These lead to better eHealth investment decisions, so better eHealth, including EHRs. Healthier Africans is the overarching goal. EHRs are an important part of achieving, but only a part, and a part with significant dependencies that need to be in place too.

EHRs are one of eHealth’s building blocks. WHO Global Survey 2015the data source for the WHO and Global Observatory for eHealth (GOe) publication eHealth Report of the third global survey on eHealth Global diffusion of eHealth: Making universal health coverage achievable, provides insights for Chapter 5.

Key findings include:

  1. Steady growth in adopting national EHRs over the last 15 years
  2. About a 46% global increase in the past five years.
  3. Over 50% of upper middle and high income countries have adopted national EHRs
  4. Much lower adoption rates in the lower middle and low-income countries at 35% and 15%
  5. Most national EHRs integrate with laboratory and pharmacy systems at 77% and 72%, with Picture Archiving and Communication Systems (PACS) at 56%.

Africa’s national EHRs match the low-income rate. Their integration with other information systems is lower than the adoption rate, so well below the global position. While some of the shortfall may be due to the definition of countries’ EHRs not matching WHO’s survey definition, so possibly understated, as the report mentions, it’s still a big gap.

Catching up needs African countries to step up their investments. It also needs investment barriers to EHRs removing. WHO says these include lack of funding, infrastructure, capacity and legal frameworks. For Africa, parallel investment’s also needed in laboratory, pharmacy and imaging services and cyber-security, eHealth governance, business cases and M&E.

Catching up alone isn’t a good investment goal. Adopting EHRs at a sustainable, affordable pace that results in healthier Africans and enables health professionals to improve their contributions are best.

eHealth can offer benefits, but new costs too. Even good eHealth can end up with limited potential due to inept or unfulfilled implementation and operation. In the British Medical Journal (BMJ), two teams of doctors in the USA offer their opposing views on EHRs’ benefits. They’re not a representative sample, but the divergent views provide insights that Africa’s health systems can expect to encounter and deal with as they pursue EHRs’ and other eHealth goals.

The team from Christus Health in Texas refers to studies showing EHRs reduce prescribing errors, shorten hospital stays and reduce mortality. This is despite its EHRs being immature, so containing some flaws. An encouraging theme’s that the team sees EHRs on an improving trajectory.

Another benefit’s helping to reduce iatrogenic harm, illnesses caused by medical examinations or treatments. Progress’s seen as avoiding the eHealth trap and delay of pursuing a perfect EHRs delay instead good and developing EHRs sooner. Preventable iatrogenic errors cause 200,000 to 400,000 deaths a year in the US, so a leading cause of death.

Related eHealth also provides benefits. Computerised Physician Order Entry (CPOE) accelerates healthcare delivery, improves efficiency, reduces the number of professionals in clinical workflows, and decreases delays, adverse events, and errors from illegible handwriting and miscommunication.

These seem valuable benefits, so what’s the problem with EHRs. The team from Yale School of Medicine and the University of California, San Francisco (UCSF) says EHRs are detrimental to physician and patient relationships. Physicians can spend twice as much time staring into a computer compared to face time with patients. He team isn’t against EHRs and eHealth. They’re more against the position where EHRs aren’t yet ingrained in physician workflows. This may be due to approaches to EHRs that have inadequate needs assessment and adjustments for end user needs.

The teams describe their views on a BMJ podcast. It seems that the maturity of EHRs’s a common thread. Choices extend across a continuum of go for EHRs now to improve them or wait until they’ve improved. At is simplest, a choice depends on the balance between the value and timing of EHRs’ probable costs and benefits. Probable’s more important than potential benefits, which are considerable, but rarely achieved, if ever.

All healthcare organisations need and rely on mass movement of patient and clinical data. Once eHealth’s used, information systems are updated and eventually replaced. Neither activity’s pain free, so the goal should be to find the least painful way to migrate data. Manual migration’s an option, but can compromise data quality, increase costs and jeopardise project timelines. Boston Software Systems recommends an electronic approach in its white paper EHR Migration Guide - Ensuring Patient Safety, Satisfaction and Clinical Adoption. It deals with:

  1. Data migration, moving data from old to new systems
  2. Data availability, providing access to patient data during and after migration
  3. Archiving to support the process of shutting down legacy systems
  4. How automation technology works
  5. Handling large volumes
  6. Overcoming automation objections.

Migration where old systems are shut down needs data to be completely extracted, validated and moved to new systems. It often encounters legacy data that doesn’t fit its new environment. Manual solutions are time-consuming and costly. Automation may be a better option.

During the switch and after, healthcare professionals need to access accurate, accessible and reliable patient data. Continuous availability’s vital for both uninterrupted healthcare and securing adoption of the new system. It’s rare for universal, simultaneous deployment across all departments and units, so there’s usually a period of double running when users access data in legacy systems, but documenting in new EHRs. The reverse may happen too, where users access new EHRs, but still documenting in the old one. A third scenario’s where some interfaces aren’t developed to pull data from legacy systems or new EHRs for secondary users.

New information systems bring several changes to some clinical practices and workflow. Accessing both legacy and new systems is disruptive and inefficient. Supplier’s streamlining solutions can be expensive and need significant time and resources to deploy. A second enterprise deployment for legacy systems heading for decommissioning isn’t financially viable either.

After go-live, decisions are needed on the systems to be decommissioned and the data to be archived. There’ll be a mix of records that need scanning into an archive solution and those to be added to new EHRs. Scanning documents or manually entering information can take months, needs significant resources and is costly. Automation can offer a better solution.

Objections to using an automation platform can arise between its suppliers and EHRs’ vendors. It’s important that healthcare organisations define and manage these relationships. They should be set at the start of procurement and sustained throughout implementation and the early stages of operation.

As Africa’s health systems move their eHealth on, they’ll have to deal increasingly with migration. It’s a good strategy to begin to develop the skills early.

The US may not be too good at EHRs. Peer 60 has released its survey results in Physicians' Take on EHRs. Its coverage includes:

  1. Ambulatory EHR adoption
  2. Main EHR suppliers for acute care participants
  3. Primary EHR suppliers for ambulatory care participants
  4. Damage control - the replacement market
  5. Top physician priorities.

The survey’s revealed some unsatisfactory findings. For 26% acute and 74% ambulatory healthcare, they include:

  1. Most physicians are highly dissatisfied with their EHR
  2. Frustrations are driven by poor usability and lack of desired functionality
  3. The EHR market for acute care facilities is consolidating quickly
  4. Fragmented Epic keeps making inroads in the ambulatory EHR market.

Most ambulatory organisations, 85%, have ERHs. Most of the other 15% are small clinics.  Measured by the number of doctors in the organisation, the vast majority of ambulatory organisations, between 75% to 100%, have EHRs. With fewer than eleven, 25% don’t have EHRs. The rest are between 95% and 1005, with 201 to 500 doctors the only one at 100%. Ambulatory organisations are more likely to have EHRs when they’ve links to larger hospitals.

About 89% of ambulatory organisations are not planning to replace their EHRs. For acute services, 91% are not planning to replace them. Acute healthcare doctors priorities for EHRs are:

  1. Patient satisfaction data: 30%
  2. Accountable care: 25%
  3. Alternative payment models: 22%
  4. Patient portal 15%
  5. No priorities: about 8%.

Cost’s the biggest inhibitor for adopting EHRs for 47% of ambulatory services. About 28% see them as inefficient. The report has a Net Promoter Score (NPS), % Promoters minus % Detractors.  Only one supplier out of nine had a positive NPS. It was 5%. The other NPS scores ranged from -24% to -73%. As a group, doctors are extremely unhappy with their EHRs.

For EHRs in acute healthcare, the range for four suppliers is 0% to -65%. The other two are -38% and -64%, so plenty of unimpressed doctors here too.

Peer 60’s overview’s that frontline user satisfaction is rare, but they have few expectations of anything better coming soon, hence the low replacement rate. The EHR supplier that cracks it will have unparalleled competitive advantage. It seems that Africa’s health systems may have three main choices: endure the dissatisfaction until a better replacement comes along, adopt a slow implementation rate that can fix some dissatisfaction before stepping on, or wait, which may be interminable and deny benefits that EHRs can offer. These are not easy decisions.

Africa isn’t awash with EHRs. The WHO eHealth Survey identified fewer than 20% of 33 countries with national EHRs and average penetration into primary, secondary and tertiary facilities well below 50%. There are also numerous EHRs in local and groups of GPs. For each of these, migrating to new EHRs can have important implications.

A white paper, Eliminate the Migration Chaos: Five Myths to Avoid in Your EHR Migration from Boston Software Systems, a data automation provider, and available from Health IT Interoperability, set out five myths to avoid. They are:

  1. Myth 1: don’t move the legacy data, start new in the next system, but the snag is, legacy systems contain valuable patient data that clinical staff rely on
  2. Myth 2: vendors will handle moving all of data with interfaces, but there’s always a core set of data that doesn’t fit pre-designed interfaces or new EHRs
  3. Myth 3: manual data entry’s good training for staff, but it has error rates between 2% to 27%, so never 100% data quality from manual data
  4. Myth 4: all data moved will be accurate, but it’s only achieved if systems are built correctly and the data structured properly
  5. Myth 5: eventually, there’ll be the advantage of a single system, but some of the legacy data structures don’t fit the new EHRs.

Believing these can lead to eHealth chaos. Avoiding them needs an understanding the unintended misconceptions that lead to migration disorder, or at best, a messy migration. Challenges are understanding and overcoming the impacts of data migration from legacy systems when their vendors may not support the change, which needs to align several vendors, stakeholders and health workers.

For Africa’s EHRs affordability is also a major constraint. Very limited eHealth resources can mean that comprehensive, high quality and total migrations don’t attract the resources they need. There’s always a crucial trade-off between new EHRs’ affordability and the resources for large-scale data migration. It’s a dilemma that requires Africa’s health systems to be realistic, which means distinguishing myths from facts.

With a Gross Domestic Product (GDP) similar to Ghana and population similar to Chad and Guinea, Jordan’s challenges for EHRs seem less demanding. Hakeem, its eHealth programme, has connected about three million people, about 50% of its population, to EHRs in public and military health facilities.

A report in the Jordan Times says Electronic Health Solutions (EHS), based on Jordan, is implementing the programme. It currently connects about 100 hospitals and primary healthcare centres. Two major hospitals, Al Bashir Hospital and King Hussein Medical Centre’ll be automated in the middle of 2017.

Hakeem was launched in October 2009. Its vision’s to create a database of patients’ medical histories across the Kingdom. It’ll include data about patients‘ tests, procedures, surgeries, diseases, allergies, medications and demographics. Completion’s scheduled for 2020.

Major clinical transformation’s one of the goals. It requires users to adopt significant changes in clinical and working practices, leading to benefits in patient safety.

There are two other eHealth developments. EHS has developed an mHealth for doctors connected to the national programme so they can access patient’s data anytime and from anywhere. About 200 doctors already downloaded the app, which’s judged to be secure. EHS’s also working on the Electronic Library of Medicine to provide Jordan’s healthcare workers and medical students with electronic, up-to-date, evidence-based and free medical information. It also aims to close the rural and urban healthcare gap.

EHS has also signed a Memoranda of Understanding (MoU) with several Jordanian universities. It includes provision of Hakeem labs for students to practise, so prepare them for future jobs.

One of Hakeem’s challenges resonates with Africa’s eHealth. Over the seven years since Hakeem started, resource availability from beneficiaries and partners has been an issue. Staff turnover’s sometimes needs addressing. EHS trains staff who sometime leave, slowing implementation because of the time needed to train replacements. Part of the solution’s creating health information committees at health facilities with a role to train new staff.

Having a national eHealth supplier like EHS with widening range of products’s an important feature of Jordan’s Hakeem. It shows the value of African countries cultivating an equivalent to help to advance their eHealth strategies and programmes.

Lateral thinking was devised by Edward de Bono. Born in Malta, one of his opinions is “Dealing with complexity is an inefficient and unnecessary waste of time, attention and mental energy.” Cardiologists don’t see it quite that way. Africa’s health systems should follow the cardiologists lead.

Algorithms using clinical data from EHRs offer opportunities for better healthcare. Cardiology services are taking advantage of these new analytic techniques, A study in the Journal of the American Medical Association (JAMA) Cardiology describes how cardiologists are develop algorithms that use readily available clinical data to identify hospital patients with heart failure. They’re diagnosing heart failure diagnosis based on discharge diagnosis and their review of sampled EHRs.

It’s leading them to better, real-time case identification so they can target interventions to improve quality and outcomes for hospital patients with heart failure. Problem lists aren’t good enough for the task. They’re useful for case identification, but often inaccurate or incomplete. Machine-learning’s seen as a way to improve accuracy, but have drawbacks too, such as implementation complexities.

The team completed a retrospective study of random 75% of hospital admissions of patients over 18 months at New York University Langone Medical Center. Data included demographics, laboratory results, vital signs, problem list diagnoses and medications to treat heart failure. Five algorithms for identifying heart failure were developed using data from EHRs.

  1. Heart failure on problem lists
  2. Presence of at least one of three characteristics: heart failure on problem list, inpatient loop diuretic or brain natriuretic peptide level of 500 pg/mL or higher
  3. Logistic regression of 30 clinically relevant structured data elements
  4. Machine-learning, using unstructured notes with over 1,118 data items in the model
  5. Machine-learning using structured and unstructured data, with 947 data items.

The problem list algorithm identified about half the patients with heart failure. It’s insufficient for real-time identification. The next two had better results, but the machine-learning ones had the best predictive accuracy because they relied on free text notes and reports.

However, it’s not a simple decision for Africa’s health systems to opt for machine learning algorithms. The research team says they’re difficult to implement because they rely on unstructured data and may need special expertise and resources. Instead, the researchers suggest that investment choices may depend on cardiologists’ clinical and operational needs. For Africa’s cardiology services, it may depend on the availability of data from EHRs too.

De Bono went on to say “There is never any justification for things being complex when they could be simple.” Selecting an algorithm to identify heart failure may not be simple. The team says there may be a trade-off between costs benefits. Complex and simple aren’t binary choices. For Africa, the five methods may offer an investment ladder to eventually reach complexity and maximise benefits.