• Economics
  • SIL-Asia reports on the Digital Health Impact Framework (DHIF)

    Economic and financial evaluations of eHealth investment options rely on modelling. The Digital Health Impact Framework (DHIF) User Manual and Illustrative Models, help health systems to set up and develop them. The DHIF is a ten-step methodology developed by Tom Jones, Peter Drury, Philip Zuniga and Susann Roth, for the Asian Development Bank (ADB).

    A blog form the Standards and Interoperability Lab Asia (SIL-Asia) emphasises the value of using examples to help users. These appear in the manual and online models. The combination of techniques and examples are from six illustrative models:

    SMS for maternal and child healthmHealth for telemedicine for a current patient catchment areamHealth for telemedicine with an expanded catchment areasMalaria surveillance and interventionCapital and leasing finance for an EHRStrategic mix and choices.

    The six models are not templates. DHIF is a generic methodology, with every DHIF model being bespoke. 

    The approach is practical and rigorous and provides a valuable foundation for our efforts at Acfee to develop an eHealth Impact Model for Africa (eHIMA). Acfee colleagues like Tom Jones, who has been involved in many related international initiatives, provide a critical overarching perspective that will help to ensure that the various frameworks emerging are both appropriate to their regions of development, while following a sound, common conceptual methodology.

    Creating options is a DHIF core skill. The first four are single options for the initial stages of digital health projects. In practice, several options for each project are analysed in these early stages. The EHR example has two options and in practice would have more.

    The blog shows two dimensions for options. They’re vertical and horizontal:

     

     

    The vertical dimension is mainly incremental and thus, relatively easy to compile. Meanwhile, the horizontal dimension is more challenging. They have significant differences to options on the vertical dimension, and not incremental.

    An illustrative model on strategic mix and choices shows positive socio-economic returns but have considerable risk exposure and affordability challenges. The comparison can support agreements on decision criteria for eHealth investment. Examples are:

    Maximum patient impactLowest riskHighest socioeconomic benefitLowest cost.

    At the ADB workshop on 31 January 2018 in Bangkok, participants identified decision criteria they would use to select which of the six illustrative DHIF models they would retain in an affordable digital health strategy and why. Their ideas are set out in the DHIF User Manual.

    Modellers new to DHIF should start small. Rome Business School has a short online DHIF course. It’s in English, and coaches modellers using their own digital health projects.

  • ADB eHealth guidance says look at the forest and the trees

    Managing and investing in eHealth’s seen as similar to forest management. Both are complex ecosystems. A Peter Drury blog from the Standards and Interoperability Lab – Asia (SIL-Asia) emphasises the large number of dynamically, interacting elements that where. Each element in the system may not know about the behaviour of the whole system. 

    Five-year strategic visions and plans help. The WHO/ITU National eHealth Strategy Toolkit provides guidance for these, but they’re not enough. Managing a complex sets of real-time elements is a greater challenge. It’s the core of Guidance for Investing in Digital Health, an Asian Development Bank initiative. 

     It’s based on how stakeholders engage, or don’t engage, with current systems, and how well, or not, they’re supported by management, technical, and workforce foundations. Investment appraisals and decisions spring from these,

    Instead of a five-year cycle, eHealth policy-makers should:

    Monitor progressAdapt to emerging challenges and opportunitiesManage expectations and investment. 

    The ADB’s Digital Health Impact Framework User Manual, linked to the Guidance, provides a methodology for these activities. It too is iterative, and addresses short and long-term requirements. 

    Pressure for quick wins doesn’t help. To counter this, the Asia eHealth Information Network (AeHIN) and SIL-Asia support work on Digital Health Governance Architecture and the Mind the GAPS programme covering governance, architecture, programme management, standards and Interoperability.

    While these are Asian initiatives, Africa can begin to adopt them. Using components that fit each countrys’ health systems is the way to start. It’ll set them on a trajectory of proven good practices.

  • A study designs a model to manage eHealth evaluation

    eHealth evaluation isn’t a common activity. A study in the Journal of Medical Internet Research, says the importance of evidence hasn’t been discussed as rigorously as the diverse research approaches and evaluation frameworks have been discussed.

    From this position, the team’s objective was to elucidate how evidence of eHealth effectiveness and efficiency can be generated through evaluation. It developed a model to help. Evidence in eHealth Evaluation comprises:

     

    It aims to show how evidence can be generated by evaluating certain aspects at each intervention phase. Assessing distinct aspects during distinct phases is a novel concept discussed in this study and requires further analysis.

    It’s consistent with Digital Health Impact Framework (DHIF) designed for the Asian Development Bank (ADB) and Standards and Interoperability Lab Asia (SIL-Asia). It also has some differences. DHIF. For example, DHIF includes optimism bias and risk exposure, and emphasises the different impacts, especially benefits, across a range of stakeholder types.

    The study implies an inconsistency between literary eHealth evaluation concepts and practices. It

    found that eHealth evaluation isn’t common in design and pretesting phases. Acfee’s view’s that it isn’t common before these, at the strategic and business case decisions stages that seek preferred options that commissions designs. It’s also rarely used at eHealth procurement stages. 

    It seems feasible to stretch Evidence in eHealth Evaluation model to include eHealth components on a wider timescale. Adding extra components within its timeline seems possible too. It is a conceptual model in its preliminary stages, so still being developed. It’s not a prescription, but a way to show a reliable progression of evidence in eHealth intervention. Africa’s health systems could build from it too.

  • Rome Business School has a short course on using the Digital Health Investment Framework

    eHealth finance and economics are core components of the  Masters in eHealth and Telemedicine Management at Rome Business School. The module includes an assignment on using outputs for the Digital Health Investment Framework (DHIF), an Asian Development Bank initiative.

    An important theme in DHIF is equipping users with the skills and knowledge to begin using it to support eHealth investment decisions. Building on this, the School now has a short course of five sessions on DHIF, all available online.

    The first course starts in February 2019. Participants can Enrol now.

    The course objectives are:

    Identify the architecture, characteristics and the roles of a DHIF modelUnderstand the concepts and methodology using illustrative DHIF modelsApply DHIF to real-life projectsReview DHIF illustrative models.

    Learning outcomes are:

    Understand and develop investment goals of health, healthcare, and digital health strategiesDefine different stakeholders’ types, user requirements and required functionalityHow to develop DHIF architecture and contentIdentify appropriate network requirements, and data and capacity dependencies from other eHealth investmentsDevelop personal skills in stakeholder engagement, human capacity building in using the DHIF and change management skills

    Contents are:

    Introduction to DHIFIntroduction to eHealth costs and benefitsIntroduction to decision makingPutting it into practice, using participants own DHIF models.

    Two organisations, Società per la Salute Digitale e la Telemedicina (SIT) and Acfee are patrons of the School's Masters in eHealth and Telemedicine Management. The DHIF short course is linked to its eHealth finance and economics module.

    The DHIF course is appropriate for Acfee’s eHealth Investment Model for Africa (eHIMA), reported in eHNA. It will enable participants from Africa’s health systems to achieve a fast start up.

  • A manual for Africa to use Asia's Digital Health Impact Framework

    Following the completion of the Digital Health Impact Framework (DHIF), an Asian Development Bank project, Acfee is completing its version for Africa. It draws directly from DHIF, and emphasises ways that Africa’s health systems can start simply and use it as a platform for increasing sophistication in appraising planned eHealth investment.

    The prototype, eHealth Investment Model Africa (eHIMA), mirrors the development track of DHIF’s forerunners that include the eHealth Impact model and the Five Case Model for business cases.  Both methodologies were less sophisticated in their original formats, and have been enhanced to meet increasing needs of decision takers. eHIMA is at the equivalent entry point for African health systems.

    eHIMA combines socio-economic , financial and accounting concepts to estimate eHealth projects’ Value for Money (VFM) and affordability over time.  These are dealt with in DHIF’s ten steps:

    Identify timescalesIdentify stakeholdersIdentify benefitsIdentify resources neededEstimate socio-economic benefits' monetary valuesEstimate socio-economic costsAdjust for sensitivity, optimism and riskCalculate net benefits, the Socio-Economic Returns (SERs)Estimate financial costs and affordabilityRefine and iterate SERs and affordability to find an optimal link

    eHIMA will guide Africa’s users in selecting which steps are the most important to being modelling and appraising for decision-takers’

    A  report on eHNA describes DHIF in more detail. It was presented to the Asia eHealth Information Network (AeHIN) conference in Sri Lanka in October.

    Acfee’s overall aim is to help Africa’s eHealth decision-takers and analysts in dealing effectively with increasingly complex eHealth investment scenarios and options. Good, affordable eHealth strategies are the starting point.  eHIMA will be available in January 2019. eHNA will post updates on progress.

  • EHR’s financial benefits may be elusive

    Acfee’s stance on EHRs is that they’re an investment in health and healthcare, not an initiative to increase healthcare organisations’ income. The Acfee eHealth Impact Database contains over 60 evaluations. A common theme is that the extra cash needed for eHealth exceeds its cash savings. Healthcare quality and productivity are the main sources of benefits. The affordability planning and management lessons are clear for Africa’s health systems.

    It seems that US healthcare may see it differently. An article in Modern Healthcare says hospitals and health systems each spent millions and sometimes billions of dollars on EHRs. Examples are: 

    Trinity Health reported a US$107.8 million asset impairment charge in 2018 to switch to a single version of Epic EHR and revenue cycle management software over four years and undisclosed costsMayo Clinic spent US$1.5 billion on Epic HERPartners HealthCare spent $1.2 billion on an Epic HERScripps Health reporting weakened financial results when started an EHR conversion budgeted at US$300 million over ten years, with estimated operating costs of US$360.5 million, 20% more than the non-recurring costsBanner’s US$45 million project contributed to a US$92 million hit to university delivery operations 2017 when it spent US$24.3 million on EHR conversion.

    Modern Healthcare says the promised clinical and financial benefits have been elusive. Some healthcare organisations have suffered financial problems when eHealth has worked against them. In particular, hospitals and health systems have faced financial stress when implementation costs drive up operating costs, a Capex Opex imbalance.

    Doctors and other clinicians have been wary of embracing eHealth too enthusiastically. Concerned that they may feel held back by it and causing clinician burnout.

    A literature review in the Journal of the American Medical Informatics Association said it revealed evidence that “Data entry requirements, inefficiently designed user interfaces, insufficient health information exchange from outside institutions, information overload, and interference with the patient–physician relationship are … factors associated with physician stress.”

    Some explanations are: 

    There’s going to be some disruption when implementing EHRs so budgeting and financial planning, including contingencies helps to avoid financial crisesTo ensure successful EHRs may need extra resources after implementation to mitigate financial risksLooking at EHRs in the long-term, rather than two- or three-year returns, can be helpfulIt’s inevitable that new eHealth, especially large-scale EHRs, will slow patient volume temporarily as providers learning to use them, so are less productivePlan for eHealth complexities that diminish returns from EHRs, including procurement costs, deployment and increases in higher ICT operating costs, higher departmental operating costs and lower productivity and lower employee satisfaction. 

    Africa’s health systems can’t afford these outcomes. Rigorous business cases, an emphasis on health and healthcare benefits and top class eHealth leadership can help to avoid them.

     

  • The pressure’s on for South Africa’s health system

    South Africa's credit rating was downgraded by Moody’s, the rating agency, on 12 June 2017. This came after Fitch and Standard & Poor's downgraded the country to junk status in March. The consequences of the downgrade are far reaching. It will affect the countries’ ability to borrow money and the healthcare system will feel the pressure too. It’ll probably knock on to eHealth too.

    Health systems and policy manager at the Rural Health Advocacy Project (RHAP), Russell Rensburg, in an article in allAfrica, warns that "We are facing a financial crisis in health and it is being ignored." He says the recession will reduce the level of taxable income as companies become reluctant to invest and create jobs in South Africa. Public spending’s also likely to shrink as more cash goes to service debt.

    So what does this all mean for the healthcare sector? There’s going to be far less money to spend on an already fragile system.

    The real life impacts will be immense and felt sooner rather than later. In September 2016, South Africa introduced new HIV treatment guidelines that now offer Antiretroviral (ARV) drugs to all people who’ve tested HIV positive. As a result, the number of people on HIV treatment will double from 3.5 million to over seven million.

    The health department's deputy director general for HIV, Yogan Pillay has said "By 2025, the health department aims to have 90% of all people diagnosed with HIV on treatment, and by then, the number of people with HIV would also have increased.  "Increasingly limited resources and competing needs are real problems to us,"

    But Rensburg says there may not even be sufficient funding to sustain existing programmes. Symptoms of the budget constraints are already showing. Specialists are still fleeing the under-resourced public sector. The South African Medical Association (Sama) says KwaZulu-Natal has only two public sector oncologists left.  Durban has none. The situation isn’t much better for other specialists with only two urologists left in the province.

    Information presented at the health budget vote in March shows KwaZulu-Natal Department of Health running a deficit of more than ZAR1-billion this year. This includes a ZA R500 million shortage for HIV treatment for 2017/2018. While KwaZulu-Natal may be the first to crumble, Rensburg warns that the public health system is failing nationally, and other provinces may not be far behind.

    The North West Department of Health has already started closing clinics and will cut 2,000, or 20%, of its public sector health jobs to curb rising costs. North West’s spokesperson Tebogo Lekgethwane confirmed the cuts are needed to accommodate smaller budgets.

    How will the restricted resources and added pressure to the healthcare systems impact innovation and implementation of eHealth and mHealth initiatives? Their value and benefits now need to be more explicit, measurable, clear and tangible than ever before to make it worth the investment in the setting of shrinking resources.

  • Africa’s GDP set to take a tumble

    Economic growth provides extra resources for governments. Then they have more to spend on public services, including public health and healthcare. eHealth, towards the end of the chain can expect more money too. It’s not good news that the International Monetary Fund (IMF) has forecast a dip in Africa’s GDP growth.

    Sub-Saharan Africa Regional Economic Outlook: Restarting the Growth Engine says sub-Saharan Africa’s (SSA) economic growth’s fragile. In 2016, it slowed in about two-thirds of the countries, accounting for 83% of GDP. IMF now estimates it to be just 1.5%, the worst performance in over two decades. For 2017, it estimates GDP growth as 2.5%, but not sustainable, driven largely by one-off factors in the three largest countries.

     Nigeria: higher public spending ahead of elections Angola: the fading of effects of droughtSouth Africa: modest improvements in terms of trade.

    While some countries can still expected to their GDP to grow between 5% to 7.5%, the underlying regional momentum’s weak, and down on trends. It’s also just exceeding population growth, a crucial drawback for health and healthcare. 

    Angola, Nigeria, and the Central African Economic and Monetary Community (CEMAC) are adversely affected by low oil prices and the resulting budgetary revenue losses and balance of payments pressures. Other commodity exporters, such as Ghana, Zambia, and Zimbabwe are facing larger fiscal deficits too. 

    Côte d’Ivoire, Kenya, and Senegal, which don’t depend so much on commodities, fiscal deficits have been high for several years as governments aimed to address social and infrastructure deficits.  While their growth remains robust, vulnerabilities are starting to emerge. Public debt’s rising, borrowing costs are up, some arrears are emerging and the banking sector’s non-performing loans are increasing. 

    The IMF says outlook is affected by drought, pests and insecurity. About half of SSA countries report food insecurity.

    Adding North Africa to the SSA forecasts also shows a slight drop in 2017 average GDP growth too, with a pick-up in 2018. It’s not all doom and gloom. An important feature of the IMF data’s that 42% of African countries are still forecast to grow more than the continent’s average. About 52% are forecast to achieve it in 2018. In 2016, GDP was more skewed, with about two thirds of Africa’s countries above average. Will this forecast deterioration translate into Africa’s eHealth having an widenning gap too?

  • 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 lifecycleHelps to manage progressions across stages of maturityImproves the rigour of evidence, optimise allocations of scarce and finite resourcesFacilitates 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 outcomesAlign these with mHealth solutions’ broader stages of maturity and evaluationIncorporate into M&E activities and match outputs to stakeholders’ evidence needsFit 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.

  • Africa’s relative poverty is increasing

    Poverty and poor health worldwide are inextricably linked, and it’s both a cause and a consequence of poor health, which traps communities in poverty. The Health Poverty Action (HPA) initiative’s clear about it, and says links between poverty and poor health are:

    Economic and political structures that sustain poverty and discrimination need to be transformed for poverty and poor health to be tackledMarginalised groups and vulnerable individuals are often worst affected, and deprived of the information, money or access to health services that would help them prevent and treat diseaseVery poor and vulnerable people may have to make harsh choices, knowingly putting their health at risk because they can’t see their children go hungryCultural and social barriers faced by marginalised groups, including indigenous communities, can mean they use health services less, with serious consequences for their health, perpetuating their disproportionate levels of povertyCosts of doctors’ fees, courses of drugs and transport to reach health centres can be devastating for poor people and their relatives who care for them or help them reach and pay for treatmentIn the worst cases, the burden of illness may mean that families have to sell their properties, take their children out of school to earn money, sometimes  by beggingCaring burdens are often taken on by female relatives who may have to give up their education, or take on waged work to help meet households’ costsMissing education has long-term implications for women’s opportunities and their healthOvercrowded and poor living conditions can contribute to the spread of airborne diseases such as tuberculosis and respiratory infections such as pneumoniaReliance on open fires or traditional stoves can lead to deadly indoor air pollutionA lack of food, clean water and sanitation can be fatal.

    The Economist has reviewed data from the World Bank.  The good news is that the number of people living in absolute poverty, defined as having less, than US$,90 a day at 2011 purchasing parity, has dropped from over 1.8 billion in 1990 to under 0.8 billion in 2013, down by about 55%. Within this considerable achievement, Sub Saharan Africa’s (SSA) relatively worse off.

    In 1990, about 15% of its population were in absolute poverty. In 2013, it was approaching 50%. Two reasons are first, absolute poverty in South Asia and East Asia and the Pacific has dropped enormously. Second, Africa’s population has expanded by about 2.5% a year, more than twice Asia’s growth rate. SSA’s absolute poverty rate has dropped from 54% to 41%, but the relatively high population growth means that more people in SSA are now living in absolute poverty.

    For many years, SSA’s been attributed with a relatively high burden of disease. Now that other global regions are pulling away from absolute poverty, it has an increasing relative absolute poverty burden. The HPA commentary indicates a heightening challenge. It’s clear where Africa’s eHealth’s focus should be.