• Economics
  • mHealth economics and finance are separate and integrated

    As mHealth continues to expand, especially from narrowly focused wearables to sophisticated clinical data and Artificial Intelligence (AI), robust economic and financial profiles are more important.

    Underlying sequences and profiles over time reveal information than can help to modify existing mHealth services and plan investments. A team from Acfee , the Johns Hopkins Bloomberg School of Public Health (JHU) and Johns Hopkins University Global mHealth Initiative has constructed a stage-based process for integrating economic and financial evaluations into business cases and M&E.

    Published this month in Cost Effectiveness and Resource Allocation (CERA), “Defining a staged-based process for economic and financial evaluations of mHealth programs” describes how eeconomic evaluations generate evidence about value for money achieved by a project. Financial evaluations provide evidence on the financing required to initiate, sustain and expand programmes and assess their affordability. Integrated economic and financial evaluation has several advantages. It:

    • Demonstrates how mHealth can be implemented concurrently across lifecycle
    • Helps to manage progressions across stages of maturity
    • Improves the rigour of evidence, optimise allocations of scarce and finite resources
    • Facilitates programme planning, implementation, efficiency, effectiveness and sustainability.

    Economic and financial data have some common features. It’s a theme important for Amnesty LeFevre from JHU, She says “There are so few high quality evaluations of digital health solutions, let alone ones that rigorously explore costs and consequences, particularly across sub-populations and geographic areas and consider the financial implications of sustaining and scaling up. Our article aims to promote evidence-based decision-making and encourage decision-makers to rely on a wider range of analyses to inform their decision on optimal resource uses.” It needs six stages:

    1.     Defining programme strategies and links with strategic outcomes

    2.     Effectiveness assessments

    3.     Full or partial economic evaluation

    4.     Sub-group analyses

    5.     Estimating resource requirements for expansion

    6.     Affordability assessment and identifying sustainable financing models.

    It recommends analysts:

    •  Prioritise activities within these stages based on programmes’ links with health outcomes
    • Align these with mHealth solutions’ broader stages of maturity and evaluation
    • Incorporate into M&E activities and match outputs to stakeholders’ evidence needs
    • Fit to time points of initiations and secure available evaluation resources for each stage.

    Acfee’s Sean Broomhead and a report author said “mHealth is a crucial and expanding part of Africa’s health systems. It’s vital we can show it’s worth it, affordable and sustainable. This rigorous methodology has an essential part to play in mHealth’s future.” Adopting the combined methodology will help to improve mHealth’s role in health systems.

  • An eHealth costs checklist is handy for business cases

    For large scale eHealth, estimating the Total Cost or Ownership (TCO) can be a tortuous process. Athena Health, a cloud service provider, has guidelines that can help. Health Care IT: The Real (and Hidden) Costs of Ownership adds to costs that are often omitted from some TCO models. It can be used as a foundation for converting into both economic and financial costs, which are related, but not the same. While the cost items included are more than US methodologies, they’re still not complete. Examples are costs of engaging and consulting stakeholders, and for financial costs, depreciation and debt servicing.

    The first task’s to set the eHealth life-cycle.

    1. One-time eHealth implementation costs include:
    • Initial software license fees                        
    • Staff training
    • Initial hardware acquisition
    • Maintenance fees           
    • Interface fees   
    • Implementation fees
    1. Ongoing eHealth costs:
    • Annual fees including upgrades                                  
    • Software maintenance fees
    • Staff training for upgrades                             
    • Future product purchases
    • Backup and disaster recovery                    
    • Server fees
    1. Ongoing operating labour costs:
    • Full Time Equivalent (FTE) clinical document management
    • FTE ICT personnel                                                 
    • FTE billing office personnel
    • FTE front office and front desk personnel
    • FTE P4P Programme support                        
    • FTE patient communications personnel
    1. Ongoing operating non-pay costs:
    • Patient statements administration       
    • Lockbox
    • Eligibility checking          
    • Electronic Document Interchange (EDI) transaction fees
    • Clearinghouse fees
    • Transcription    
    • Paper claim storage
    • Patient no-shows                                                
    • Billing under-performance
    1. Other costs not in Athena’s checklist include:
    • Change management, including workflow standardisation
    • Project management
    • Risk exposure where up to 70% percent of healthcare providers are dissatisfied with their EHRs and healthcare professionals spend more time on documentation.

    Estimating benefits is not as easy as estimating costs. Many are the potential to redeploy numerous small amounts of resources across healthcare activities. Many are intangible and need sophisticated techniques. They include:

    1. Increased efficiency and quality, such as fewer interruptions and distractions
    2. Better care coordination among providers
    3. Agility and an ability to scale eHealth up or down quickly as organisation evolve
    4. Responsive to changes in reimbursement models, reporting, clinical requirements, and other
    5. regulations
    6. Regulation compliance by having the right reporting, data and workflows in place to meet new mandates and standards
    7. Integration ability to build effective, low-cost links to clinical partners, such as laboratories, imaging pharmacies, to exchange information, and build and connect with expanding mHealth programmes
    8. Mitigated risks with high adoption and user satisfaction where up to 70% percent of healthcare providers are dissatisfied with their EHRs.

    Costs and benefits over timescales need converting into Net Present values (NPV) using Discounted Cash Flow (DCF). For TCOs for public healthcare organisations, a discount rate of about 3%’s appropriate.  There’s a recognised tendency for estimators to suffer from optimism bias. Estimated costs need adjusting for it. For eHealth, it can be between 40% and 100%.

  • Africa’s eHealth financing’s not typical – unpacking WHO's 3rd Global Survey on eHealth

    Sustainable eHealth is a goal for Africa. Affordability is a crucial component. 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. 

    Chapter 1 provides insights. It shows a profile of four main sources. Africa’s is very different, confirming one of its biggest challenges, securing sufficient sustainable eHealth finance. The comparison is:

    The telling challenge is that Africa’s reliance on donor and public finance is nearly as much as the global rate for public finance. It’s widely recognised in Africa that this is not sustainable enough, but realigning it to match the global profile more closely isn’t realistic in the medium term.

    Instead, a strategy of seeking donor support for non-recurring resources that matches Africa’s eHealth priorities seems a better option. This isn’t easy either. Africa’s eHealth needs investment in a wide range of capacities and infrastructure to expand and deepen its foundations. This can be less attractive to donors who have their own priorities that are often for more visible and tangible projects.

    The other important feature is that PPP is close to the global average too. PPP often has high operational costs and limited risk sharing and almost no risk transfer. It’s attractive to start up big scale eHealth programmes, but its annual operating costs can be extremely rigid and onerous. If WHO’s survey shows Africa moving towards PPP instead of the more demanding initiative of expanding public finance, it signals a need for rigorous financial and risks assessments as part of a robust business case before proceeding.  

    eHNA’s posted on an extreme example of a crashed PPP. The health system believed it had transferred the risk, but the legal system didn’t see it that way.


    Image from the global eHealth observatory report 

  • Africa’s GDP’s shrinking a bit

    Like many countries, the scale of Africa’s healthcare, so eHealth, depends on Gross Domestic Product (GDP) growth, population expansion and political priorities. The report from the IMF Regional Economic Outlook Sub-Saharan Africa Time for a Policy Reset, as it title suggest, shows some signs of concern, but it may not be all gloom.

    Comparisons of GDP and GDP per head since 2004, show that the estimate growth for 2017 might be slowing down compared to earlier highs. While the estimated growth rate of 4% is 1% higher than 2016’s 3%, it’s lower than the average growth rate of 4.3% since 2004. It might not be much, but it could constrain eHealth investment.   

    GDP per head shows slightly different position. Estimated growth in 2017’s 1.6%, 1% up on 2016’s growth, but a shade closer to the average of 1.8% since 2004. As Africa’s population continues to expand, GDP isn’t keeping up. The average GDP growth per head’s less than half the overall GDP growth rate, but the curve is wide.

    A simple comparison of 45 African countries’ changes shows:




    2017 to 2016





    GDP per Head








    2017 to Average Since 2004





    GDP per Head








    It’s not possible to say that Africa’s GDP’s on a new trajectory. The IMF seems to think it is. It says it’s “Time for a Policy Reset Economic activity in sub-Saharan Africa has weakened markedly, but, as usual, with a large variation in country circumstances.”  This may extend to the resources for eHealth. The next set of IMF numbers might shed more light.

  • How will Africa’s falling GDP affect eHealth?

    Recent high growth in Africa’s Gross Domestic Product (GDP) has increased the possibility of eHealth being more affordable. It looks set to take a bit of a knock. An article in Nigeria’s Business Post describes Sub-Saharan Africa’s (SSA) latest, low GDP forecast from the World Bank.

    While GDP growth may range across SSA countries, the average forecast’s a drop to 1.6%, the lowest level in twenty years, and nearly half of 2015’s 3%. The weak performance is mainly due to deterioration in the continent’s largest economies: Nigeria and South Africa. Together, they comprise half the region’s output. Ethiopia, Rwanda, and Tanzania have continued to achieve annual average growth rates of over 6%. Côte d’Ivoire and Senegal have recently joined the ranks of top performing countries. Resilient SSA countries show more diversified export structures. They’ve also made more progress on structural reforms, business regulation, rule of law and government effectiveness. It’d be good to see eHealth adding to healthcare effectiveness.

    Adjusting to low commodity prices has affected several commodity exporters as vulnerabilities have increased. Measures are now needed to:

    1. Strengthen domestic resource mobilisation
    2. Reduce over dependence on resource-based revenues
    3. Increase agricultural productivity, central to transforming SSA economies
    4. Address the quality of public spending and the efficiency of resource, an objective more critical than addressing the level of spending.

    Stimulating countries eHealth supply and developers could be part of this, especially if their solutions can be transferable and exported. Could it be the key to avoiding eHealth affordability and sustainability risks?

  • Africa’s economic growth’s a lot more than its forecast population growth

    If smart kids found out that their parents were having a pay rise of about 4%, they’d want their pocket money to go up by the same rate. It seems a reasonable proposition from the children’s view.

    Acfee’s data base shows that Africa’s economies are forecast to grow by about 4% a year over the next few years. It seems reasonable that Africa’s health systems should expect a good chunk of that to be added to the health and healthcare budget. An extra 4% a year’ll go a long way towards the extra cost of Africa’s average population growth of about 2.5% a year.

    In turn, eHealth enthusiasts could make a case for a good share of the extra Gross Domestic Product (GDP) allocated to healthcare to find its way into more and better eHealth. For this proposition to look attractive, eHealth investment must contribute directly to better health and healthcare for Africans. It means effective eHealth and Africa’s numerous Points of Care (POC) where patients meet healthcare professionals and other health workers.

    It also means that eHealth which provides information for health policy makers, strategists, planners, mangers and surveillance teams has to show substantial benefits in the way they sue the information to improve health and healthcare. This might have a longer timescale than the benefits from POC eHealth. Public health and surveillance teams will be able to achieve different and more direct benefits of more eHealth for Africans to healthcare’s managerial sector.

    Smart parents would probably tell their smart kids that there’s lots of other important things to spend their pay rise on. Africa’s governments have many other priorities too. Education services need more money to keep up with increasing populations, so has a good claim on extra GDP. Government debt may need reducing too, but it may not be a priority for all African countries. Acfee’s database has estimates from the International Monetary Fund (IMF) showing an average of less than 50% of GDP for 28 African countries may be falling slightly as a percentage of GDP over the next five years.

    Both education and healthcare could expect substantial shares. Let’s watch how it evolves.

  • Africa’s eHealth could have big economic gains

    Healthcare’s a merit good. It means that it should be available as a concept of need, rather than ability and willingness to pay. There are two main justifications. One is that people may under-consume it. The other is that accessing it can have benefits to other people.

    This implies socio-economic benefits. For economic gains, there are direct examples for employers such as fewer days off work. For some communities, the economic gains are considerable, such as preventing onchocerciasis, commonly called river blindness, which can enable agrarian communities to sustain their workforce, so food production.

    There’s a less clear relationship on countries Gross Domestic Product (GDP). A study reported in the USA’s National Bureau of Economic Research (NBER) found that “While health improvements may well raise worker productivity, many potential interventions in developing countries will also be accompanied by the side effect of a rapidly growing population, which will have negative economic effects over a significant time horizon. An understanding of the demographic dynamics that accompany health improvements therefore suggests complementary policies and investments… Improvements in health may temporarily (or even permanently) reduce income per capita.” This reveals the complexity of the links between health and national economics.

    However, just because it’s a bit of a messy link that’s difficult to measure, doesn’t mean that a positive link doesn’t exist. Desmond Tutu said it’s important to “See that there is light despite all of the darkness.” While he said it about hope, it applies to unmeasurable relationships too, and eHealth has an important role in the impact of health on economies. It’s also important not be carried away by eHealth’s potential benefits. They usually exceed considerably the probability of benefits. Hope has to be a good bit lower than optimism.

    An article in IT News Africa aims to shed some light on the topic for Africa. It anticipates its positive effect on Africa’s healthcare.  It sees increased investment in both pharmaceuticals and eHealth to include:

    1. Local business flourishing in South Africa, Kenya, Ethiopia and other emerging economies
    2. A rising middle class
    3. Introduction of local manufacturers
    4. Support from national governments
    5. Handheld medical devices
    6. mHealth
    7. Micro health insurance
    8. In-country manufacture of drugs
    9. Improvements to client care delivery models.

    The 2016 Africa Healthcare Industry Outlook from Frost & Sullivan says global shift towards value-based healthcare is improving Africa’s healthcare. It expects improvements in therapeutics, medical products and eHealth. This will help healthcare stakeholders and policy makers to design solutions and strategies that meet the demands efficiently.

    An example is low-cost drugs and equipment designed specifically for Africa. These will substitute traditional, costly options.

    There are a few long-standing snags. They include the large health worker deficit and eHealth affordability. Taken together, these perspectives combine into a the continuing challenges that Africa’s health ministers constantly fave.

  • Africa's GDP numbers might not be up to scratch for healthcare

    Gross Domestic Product (GDP), an estimated value of all the goods and services produced by a national economy has been a common measure since it was devised in 1939 by Maynard Keynes, the famous economist who gave his name to Keynsian economics. He described it as calculating the “Maximum current output we are capable of organizing from our resources.” This’s GDP. 

    Since then, it’s become a gold standard, with numerous tables comparing countries GDP and its share spent on healthcare published by global organisations such as the World Bank and WHO

    An article in African Affairs, the journal of the Royal African Society Africa’s GDP numbers can be misleading, especially for agrarian economies using a statistical tool designed for industrial countries. African countries also suffer from weak information systems. These conspire to produce two biased views. One’s the distorted values for low income countries. The other’s the limited knowledge about poor people. Nigeria’s and Ghana’s GDP revisions in the first have of the 2010s are attempts to correct for distortions. 

    The World Bank’s World Development Indicators has access to data that shows that Africa’s average GDP per head might be about US$150. If African countries have higher GDPs per head, it means that healthcare’s spending share’s lower than assessed. This could be a justification for spending more on healthcare, so more on eHealth. Will it be?

  • Denmark's identifying and sharing some good eHealth practices

    Evaluating eHealth’s generic impact in Africa’s been a bit like the search for the elusive needle in the conceptual haystack. eHealth may have huge, visible potential, but finding it’s probable generic net benefit over time, the difference between estimated costs and benefits, hasn’t found much traction. Instead, modest efforts have been directed to evaluations of specific types of eHealth in specific settings with limited transferability. 

    In 2014, Denmark planned to break through the barrier. It’s

    • Share best practices
    • Learn from one another
    • Increase the use of welfare technology, which is eHealth and eWelfare.

    It’s financed by the government’s Denmark’s Digital Welfare Strategy 2013 to 2020 within a budget between DKK 1.065m ($144m) and 1.5m, (US$217m) for each project. The allocations of the DKK 4.065m budget:

    • Evaluation of digitally supported work on early detection, DKK 1.065m
    • Use of return systems for the prevention of pressure ulcers and injuries, DKK 1.5m.
    • Longer home together will test and evaluate of an intelligent sensor based alarm system for home, DKK1.5m.

    Early detection’s led by the municipality of Aalborg. It’ll test solutions for the early detection of health issues in the elderly, including automatic alerts for medical professionals. The municipality of Aabenraa leads on systems that automatically turn bed-ridden patients, so reduces the number of medical staff needed. Using of sensors installed in homes to support home care for peoples with is being evaluated by the city of Aarhus. The next series of applications is expected in mid-2016.

    Low and Middle Income Countries (LMIC) have different eHealth priorities, so the need for evaluation data. In 2011, WHO published findings from a review of evaluations of three types of eHealth in Low and Middle Income Countries (LMIC). It included:

    • Systems facilitating clinical practice
    • Institutional systems
    • Systems facilitating care at a distance. 

    It found that large randomised trials provide strong evidence of eHealth’s efficacy and its potential impact on outcomes, but, highly controlled studies fail to answer questions about:

    • eHealth’s reach into vulnerable communities
    • Can eHealth systems be adopted, scaled up and maintained outside the environments in which they were originally studied, the conundrum of transferability.

    It proposed new approaches to evaluation that emphasise qualitative and quantitative methods, community-based participatory research, and organizational theory in addition to controlled trials and ensure that eHealth’s relevance and flexibility to adapt to different settings. Evaluations comprising several sites are expensive, so a constraint. Their benefits need weighing against two other approaches. One’s larger numbers of smaller and innovative, less definitive evaluations of eHealth adapted to different cultures and environments. The other’s step-wedge designs where eHealth’s gradually rolled out to new sites. 

    It’s vital that Africa’s health systems adopt this advice to improve their eHealth investment. It seems that eHealth decision-takers may have to keep waiting for facts provided by their eHealth evaluation needles, while their health hay stacks keep steadily expanding. It’s not just time and tide that doesn’t wait.

  • What's needed for DRG's in South Africa's NHI?

    A critical theme in South Africa’s National Health Insurance initiative is changing the way that hospital services are financed. Switching to Diagnosis Related Groups (DRG) was part of the original Green Paper in 2011, and remains firmly in place in the updated version released in December 2015. It’s not a simple switch, and needs several building blocks to work effectively.

    It’s also a proven financing methodology. Many countries use DRGs, and have developed their bespoke versions. Originally designed as a quality assurance tool in the USA, DRGs were used by the Medicare Program to reimburse hospitals using prospective prices from 1983. It had 470 DRGs across 23 Major Diagnostic Categories (MDC), each of which can include both surgical and medical services. As combinations of WHO’s International Classification of Diseases (ICD), currently ICD-10, they initially didn’t reflect the severity or complexity of the conditions, and adjustments for these came later. There are now over 750 DRGs in use for 26 MDCs, depending on the version and country. Some DRG prices distinguish day case from inpatients. 

    About half the DRGs are medical, 47% for surgery in MDCs, plus 2% for pre-surgery and 1% for surgical cases not in MDCs and two DRGs for an invalid principal diagnosis and ungroupable workload. About 3% of DRGs aren’t in an MDC. It’s roughly a 50:50 split between medical and surgical cases.

    Prospective DRG prices are seen as a way to control healthcare costs. It requires hospitals to manage their unit costs and annual expenditure within these. So why’s it not that simple? DRGs don’t extend across all hospital activity and need time to catch up with new medical techniques. These can either have their own reimbursement rates, or take so long to set that they can be a brake on investment, a claim for a slow take up of telemedicine. A recent example’s the proposal in 2015 from the American Association of Hip and Knee Surgeons (AAHKS) to the Centers for Medicare & Medicaid Services (CMS) to modify or establish a new MS-DRG for total hip arthroplasty (THA) cases involving patients with hip fracture. For 2016, CMS has released DRG version 33, one a year since 1983, including eight changes to existing Medicare Severity (MS) DRGs.

    Since 1983, DRGs have almost gone global. As a proven reimbursement method, many countries have refined them to match their health systems. The UK and Ireland have Healthcare Related Groups (HRG). The Nordic countries have their NordDRG. These reflect health systems’ bespoke characteristics. This includes their different healthcare models and their specific cost structures. 

    Setting prices for DRGs often starts with existing unit costs. This needs a costing system with two main methodologies. One’s Total Absorption Costing (TAC) to allocate and apportion expenditure to unit costs. The others a variable and semi-variable costing methodology to identify expenditure changes arising from changes in workloads. Semi-variable costs are the most challenging to compile. Both methodologies rely on sound workload data and ICD 10 coding.

    Using TAC throws up a specific challenge for healthcare. Direct costs that can be allocated to specific patients are a small proportion of total costs, maybe less than 10%. This creates the risk of large skewed unit costs derived mainly from using formulae for apportionments. One way to minimise these inconsistencies it to create cost pools for the MDCs or specialties where direct costs can be increased significantly. Each MDC can have its own apportionments on to workloads. This minimises skewed results, but doesn’t avoid them, so comparisons between hospitals’ will still reveal some odd numbers.

    For South Africa’s switch to DRGs, some important activities are needed that apply lessons learned from other health systems. They include:

    • Test the accuracy and completeness of hospital’s workload data, including duplication challenges as reported in eHNA, and minimising the workload assigned to DRGs for invalid principal diagnoses as discharge diagnoses and ungroupable workload
    • Test hospitals’ ICD-10 readiness to avoid the USA’s unhappy experiences reported in eHNA
    • Design the DRG grouper needed for reimbursement, including DRG choices for patients with more than one DRG for their hospital stay
    • Set up reimbursement models for hospital services that don’t fit the DRG model
    • Design and trial a national hospital costing methodology that reconciles workloads x unit costs to total expenditure
    • Refine the costing methodology
    • Run a parallel DRG financing model alongside the existing model to identify large swings
    • Design an interim DRG financing model to minimise financial risks
    • Estimate and monitor changes to hospital costs and the impact on total health service spending and finance.

    With the NHI’s fourteen-year development timescale, now’s the time to start these. An early start provides time to iron out data idiosyncrasies and will help to minimise risks, which are always prevalent when financing models change. eHNA’s looking forward to reviewing these soon.