African countries setup Country Health Situation Rooms for better health monitoring
Two weeks ago, I was fortunate to participate in a workshop in Ethiopia hosted by the African Union, Africa CDC and UNAIDS. The workshop aimed at strengthening the Country Health Situation Room initiative and roll-out across African countries. Its goal is to support better use of health data and help countries keep populations healthier by improving their response to infectious diseases and epidemics.
Kenya was the first African country to adopt the Situation Room in 2015. A further six countries – Cote d’Ivore, Lesotho, Namibia, Zambia, Uganda and Zimbabwe – have launched their Situation Rooms and are currently at different stages of scale-up and roll-out.
The Situation Room software integrates health data from multiple sources such as the DHIS2 and logistics management information systems (LMIS) at a country level. Data are presented visually to help countries track progress and identify gaps in key health indicators. The customisable interface allows countries to design their Situation Room around their health areas of interest and user types.
Matthew Greenall’s case study on the Country Health Situation Rooms describes the progress so far. Achievements include;Enhanced collaboration between different health programmesImprovements in health decision makingImprovements in data qualityIncreased data use for decision makingImproved data sharing between stakeholders at national and regional levels
Important challenges are also identified, such as;High turnover of staff and leadership compromised progressOperational and budgeting constraints interrupted roll-out in some countriesPoor quality of data at sub-national levelsOwnership – a strong desire for countries to host the software themselvesMaintenance of the Situation Room software requires strong technical support
The Health Situation Room is a bold step for the participating African countries. We look forward to reporting the progress of this important eHealth contribution to health systems strengthening.
- 154 views
- March 05, 2019
- Ameera Hamid
Medopad aims for doctors’ and patients’ information to reach beyond healthcare
Based in London, the Medopad mission’s to build solutions that provide the right information to patients and doctors when patients are beyond healthcare settings. It says this’s 95% of the time.
Activities that its data support includes:Better medical diagnosesEnhanced treatmentsExpanded professional knowledgeEmpowered publicFaster and better collaboration of medical teams.
It claims its services are used by the “world’s leading healthcare providers.”
Medopad’s examples include four major London Hospitals:Royal Free LondonGuy’s and St Thomas’Bart’s Chelsea and WestminsterHospital Corporation of America (HCA) a private healthcare provider.
Its data range includes:Medication trackingBlood glucose monitoringSP O2 logsWalk testsWeight measurementSymptom logsAfter care videosSupport groups.
These are for four main conditions:Rare diseasesMetabolic diseasesCardiovascularCancers.
Health insurers use Medopad to reward policyholders for healthy behaviour. Benefits include increased retention, lower risk and bespoke policies.
Pharma’s a development project. Three goal are to use Medopad’s real-time data to develop more effective drugs, accelerate medication trials and to close the gap between suppliers and hospital.
How long will it be for Medopad to be used across Africa? Does its emphasis on tertiary hospitals and rich countries’ health insurance mean that Africa’ll be towards the bottom of its priorities?
- 391 views
- December 03, 2018
- Tom Jones
Zanzibar’s eHealth aims to connect its 24 hospitals
Zanzibar, a semi-autonomous Tanzian region in the Indian Ocean, has successfully installed a national ICT programme. It’s the backbone of social services digitisation. A report in IPP Media says it’ll provide broadband to its citizens across the archipelago and connect all 24 hospitals in region.
The government has set up a data centre to house medical information. It supports the eHealth objective of improving delivery of a range of social services.
It also provides a platform to develop eHealth programmes to:Share patient informationRemote interpretation of test resultsRemote diagnosis.
The plan’s to use the expanded connectivity to improve healthcare and social services quality. There’s a more sophisticated objective too. It’s to stimulate economic growth by unlocking entrepreneurial potential. This can create exponential eHealth investment trajectory that all Africa’s health systems could replicate.
- 316 views
- November 21, 2018
- Matt Jones
What does eHealth have to do for radiology services?
Radiologists are in short supply. Radiology workloads and demand are rising. A report from Digital Health explores the opportunities to use AI and Radiology Information Systems (RIS) in the UK’s NHS to fill the gap. It identifies essential requirements for national eHealth too.
Two solutions are proposed, both needing RIS:
Sharing reporting workloads across healthcare organisations
Using AI to automate some of the clinical workload.
Current images and workflow sharing relies on an Image Exchange Portal run by Sectra. It’s fast, but seems it needs replacing to meet radiologists’ needs of:Knowing when an image is there for reviewA single system that displays their own images and other clinicians’ images for individual patientsAccess to each patient’s reporting history and images needed for full and useful reports.
This needs a specific organisational structure, a lesson for Africa’s health systems. In the days before England’s National Programme for IT (NPfIT) was abandoned, radiology information could be shared across each of England’s five NPfIT regions.
Since then, smaller geographic consortia have emerged to procure Picture Archiving and Communications Systems (PACS) and RIS from single vendors. It achieves lower costs, smoother, more efficient workflows and makes their sharing easier. Patients, radiologists and organisations outside these consortia don’t benefit.
Vendor-neutral standards are the solution. Two, Soliton and Wellbeing Software, provide solutions share radiology reporting across several sites with different RIS vendors. Their impacts are constrained because there isn’t a single or unified procurement organisation.
Is RIS becoming obsolete? EPRs and PACS may be able to deal with scheduling and remote reporting. Some radiologists see it differently. They may be increasingly dependent on RIS.
AI may be a solution too. It’s already dealing with some basic reporting. Wellbeing has a platform for an AI algorithm to report directly into its RIS.
Agfa uses the term Augmented Imaging (AI). It’s exploring the potential for its AI to automate some administrative tasks. Algorithms are already available to detect TB on chest X-rays. Partnering’s already in place with hospitals and research institutes that need Agfa’s workflow engine to develop their own algorithms.
Lessons for Africa’s eHealth are clear. Radiology needs its own eHealth engagement, strategy, plans and procurement.
- 221 views
- July 04, 2018
- Tom Jones
GIS software helps optimise health efforts
Graphic information systems (GIS) software could change the way countries tackle public healthcare issues. GIS helps capture, store, combine, analyse and display aggregated data from censuses and national health information systems and then overlays this data onto regional maps. This visual representation of data then allows departments of health and ministries to better manage resources and plan accordingly.
A great advantage of using GIS technology in healthcare application is the spatial dependency of health related factors. Several countries and organisations have already started to invest in GIS programmes. In the United States, the Centre for Disease Control (CDC) launched its 500 Cities Project, which aims to provide geographic data on the distribution of chronic disease risk factors. In South Africa, the South African National Aids Council (SANAC) launched the Focus for Impact Project, which aims to identify populations most at risk in areas most severely affected by HIV and TB.
The hope is that by better visualising and understanding the geographic distribution of health variables, health departments and planners will be able to plan public health interventions more effectively. GIS software helps with this by answering 2 key questions;Where are the high burden areas? – by overlaying routine health data on geographical regionsWhy is it a high burden area? – by profiling epidemiology and associated risks using secondary data and community dialogue
This in turn allows health departments and health planners to identify;Who is at risk in this high burden area?What interventions can help reduce this burden?
To improve the overall health of our communities, access to these kinds of services is vital. Further investment into GIS programmes could reveal other beneficial use cases for the healthcare industry, improve overall efficiency and better manage the cost burden of the healthcare system.
- 602 views
- April 30, 2018
- Ameera Hamid
Can Africa adopt a modern master patient index?
Paper patient administration and medical records can be unreliable in sustaining patient identification. Overcoming their limitations needs a sound Master Patient Index (MPI) and effective patient identification as a foundation for dependable eHealth. A white paper from Verato, an MPI vendor, describes a way to do it.
A thesis in The Future of Healthcare Depends on a New Architecture for Patient Identity Interoperability has five components:Healthcare will involve extensive co-ordination across the full care continuumThe ability to access patient information is the cornerstone of co-ordinationResolving patient identities across disparate systems and enterprises is critical to accessing informationCurrent MPI technologies can’t resolve patient identities consistently enough or well enough to support emerging information needsMPIs patient identity resolution technology must support the new needs as part of a highly accurate, national patient identity resolution service.
Africa’s health systems can apply these criteria to their strategic and procurement choices. They apply to all types of eHealth, not just EHRs. It’s a core requirement for improving healthcare efficiency. It supports a shift from point-in-time service towards effective healthcare co-ordination too.
Three themes are needed for effective access to patient information:Agreed rules and policies for sharing patient dataStandardised access protocols and content in EMRs and EHRsPatient identity matching.
A unique national patient ID number is seen as supporting these. But Verato sees this as logistically
Impossible, politically untenable because of privacy implications and would not help link people to pre-existing medical records. Relying on basic demographic identifiers such as name, address, birthdate, gender, phone, email, and social security numbers aren’t a solution because they’re prone to error when patients register at receptions and can change over time. About 8 to 12% of people may have more than one identity in any given hospital system, with actual medical histories spread randomly across them.
MPI matching techniques was invented in 1969, and obsolete. Verato sees the solution as a pre-built, cloud-based, nationwide MPI that healthcare organisations can plug into. It can avoid the need for extensive algorithm tuning, data standardisation, data governance, data cleansing, or data stewardship. It can help to achieve better compliance with data standards.
As Africa’s eHealth moves on, the concept can be assessed as an investment option. If it’s not, then an option to deal with the limitations of conventional MPIs may be needed.
- 538 views
- February 23, 2018
- Tom Jones
Top ten algorithms that can help healthcare
As algorithms become more prevalent in eHealth, it’s important to have a clear development path for their use. Two core principles are:No single algorithm works best for every problemA learning a target function (f) maps input variables (X) to an output variable (Y), so: Y = f(X), used for predictive modelling.
An article by James Lee in Towards Data Science sets out ten top algorithms. They’re:Linear regression, a long-standing techniques from some 200 years ago, but a good starting pointLogistic regression, suitable for binary classification problems and their two class valuesLinear discriminant analysis, where prediction rely on calculating a discriminate value for each class and making a prediction for the class with the largest valueClassification and regression trees represented by a binary treeNaive Bayes, a simple, powerful algorithm for predictive modelling using two types of probabilities, one of each class, the other the conditional probability for each class given each x valueK-Nearest Neighbours (KNN), a simple and effective algorithm, where predictions are derived from new data points by searching entire data sets for the K most similar instances, the neighbours, and summarizing output variables for those K instancesLearning Vector Quantisation (LVQ), a KNN relative, and an artificial neural network algorithm enabling choices of the number of instances to hang onto, learning precisely what the instances should look likeSupport Vector Machines (SPV) are possibly one of the most popular, using a hyperplane to separate points in input variables spaces by their class, either class 0 or class 1Bagging and Random Forest (BBR), another popular algorithm, called Bootstrap Aggregation or bagging, and can estimate quantities from data samplesBoosting and AdaBoost, an ensemble technique aiming to create strong classifiers from several weak classifiers by building a model from training data then creating a second model that attempts to correct the errors from the first model.
Selecting algorithms in eHealth uses, four questions need answering, what’s:The size, quality, and nature of the dataThe available computational timeThe urgency of the taskThe data to be used for.
The answers aren’t easy to find. Lee points out that experienced data scientist can’t tell which algorithm’s best before trying different ones. It seems that Africa’s eHealth needs time to ponder these before settling on a preferred short list.
- 314 views
- February 08, 2018
- Tom Jones
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.
- 462 views
- December 18, 2017
- Tom Jones
Dell offers better access to imaging data
Modern eHealth can provide mountains of clinical data. Storing and accessing it effortlessly in real-time’s an increasing challenge. A whitepaper from Dell EMC, available from EHR Intelligence, describes a way to do it.
Key Strategic Technolgies (sic) to Improve Access to Clinical Data promotes two principles for PACS. One’s that storage infrastructure shouldn’t need redesigning every time new data’s added. The other’s to have a Vendor Neutral Archive (VNA).
Affording a fully-fledged solution can be a challenge for Africa’s tight eHealth finances. Dell EMC proposes a phased approach that supports future VNA deployment. It is flexible enough to support a wide range of performance demands such as data analytics, expansion into private, hybrid, or public clouds and changing clinical workflows.
It’ll need Africa’s eHealth programmes to partner with infrastructure development vendors who can:Scale local architecture without downtimeMaintain daily performanceReduce or eliminate future migration burden.
These will help to achieve several objectives that improve healthcare quality:Integrate imaging with other eHealthEnable doctors to taking clinical decisions using the most pertinent, complete, accurate and timely patient data.
Can this find a place in Africa’s eHealth strategy? The principles fit all types of clinical data.
- 526 views
- December 07, 2017
- Tom Jones
Managing high risk populations’ health needs better information
Successful population health management need health organisations to learn and know how to manage risks, outcomes, utilisation and well-being of high and increasing risk communities. Components Necessary for Managing High-Risk Population, a report from Cerner, available from EHR Intelligence, sets out ways that organisations can use information to manage people’s care as part of health risks cohorts and identify opportunities to reduce avoidable costs.
The report says about 5% of the population are high risk. Another 20% are grouped as rising risk. Globally, these health risks are increasing. In a report, on global health risks, WHO says “Health risks are in transition: populations are ageing owing to successes against infectious diseases; at the same time, patterns of physical activity and food, alcohol and tobacco consumption are changing. Low- and middle-income countries now face a double burden of increasing chronic, non-communicable conditions, as well as the communicable diseases that traditionally affect the poor. Understanding the role of these risk factors is important for developing clear and effective strategies for improving global health.”
Cerner’s report focuses on care management requirements and patients. The principles apply to health promotion and illness prevention too. Selecting the right people for care management plans is essential to improve their health and enable healthcare to cost outcomes. Cerner proposes three components:Risk stratification strategiesHealth IT needs for managing high-risk populationsChoosing the right care management approach.
These are supported by six eHealth requirements.Longitudinal healthcare recordsChronic condition and wellness registries for patient cohortsCare management and co-ordination systemsLongitudinal plansData analytics and modellingReferral management system.
Linked to information on social determinants of health, some of this approach can support health interventions in high risk communities in low and middle income countries. It could include local data and predictive analytics to identify changes communities’ behaviour, and needs and demands for healthcare and related services such as education and social care.
- 416 views
- November 15, 2017
- Tom Jones
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