copy the linklink copied!2. New ways of delivering care for better outcomes

Current ways of delivering care are ill-suited to emerging health needs

Health care is often provided inflexibly in a fragmented and specialty-based way. This is particularly ill-suited for serving the increasing number of people with multi-morbidity and complex needs in OECD countries.

People with multi-morbidity manage a high volume of information, interactions with various providers, and self-care tasks; they need to coordinate, synthesise, and reconcile health information from multiple sources and about different diseases; and their position at the intersection of multiple health issues requires self-advocacy and expertise. Multi-morbidity also often occurs among the elderly, the disabled or people in lower socio-economic classes, who might find it particularly difficult to navigate current health systems and accomplish all of these tasks related to managing their health. In Denmark, for example, multi-morbidity affects every other person older than 65 (Schiøtz et al., 2017[4]). Among people with lower secondary education only, the prevalence is double that among people with postgraduate education (ibid.). The co-occurrence of multi-morbidity with difficult socio-economic circumstances make the care needs of such population groups particularly complex.

ICT and data present an opportunity to make care more effective and efficient

People with complex needs account for a disproportionate share of total health care utilisation and expenditure in OECD countries. In United States, for example, health care costs of people with three or more chronic conditions are almost twice as high as in the average adult population and costs of people with three or more chronic conditions and disability are more than four times higher (Hayes et al., 2016[7]).

A complexity-based stratification of the population of the Spanish region of Catalonia (see Chapter 8 on system governance, stewardship and resource allocation for further details) found that, compared to people below the median on the complexity scale, people in the top percentile of the complexity score were 27 times more likely to have an emergency hospital admission (31.9% vs. 1.2%), had 15 times the number of primary care consultations (31 consultations on average per person and year vs. 2.1) and 61 times the pharmaceutical expenditure (EUR 1 394 on average per person and year vs. EUR 23) (Monterde, Vela and Clèries, 2016[8]). A recent cost-of illness study in New Zealand found that co-morbidity resulted in greater health care expenditure than the expected sum of the present conditions in isolation, with 24% of all health care expenditure attributed to this super-additive feature of complexity (Blakely et al., 2019[9]).

The enhanced use of data and deployment of the knowledge that can be generated from them present a great opportunity for improving the care for people with complex health needs. Through its ability to generate and analyse vast amounts of data, ICT can help improve care for these people in multiple ways, as described below. Effective uses of ICT and data for improving care for people with complex needs are necessary to promote financial sustainability of health systems through deploying services where they generate the most benefit, which drives system efficiency. They also increase efficiency by preventing more costly future service use, such as emergency room visits and hospital admissions.

However, needs-based care delivery does not necessarily imply cost savings; it can also result in increased secondary and tertiary care utilisation and higher aggregate costs, in particular in the short and medium term. This was the case, for example, in the Personalised Integrated Care Programme in the United Kingdom, a pilot project of personalised support to older people who are at risk of a future emergency admission. While the scheme delivered positive outcomes as reported by patients and staff, it resulted in a rise in hospital activity (and costs) for enrolled participants in the 16 months following implementation. The most likely reason for this increase is that participation led to previously unidentified health needs being addressed (Nuffield Trust, 2019[10]). It is possible, however, that this increased hospital activity prevented more serious morbidity in the future and, in some cases, perhaps premature death (but longer-term effects were not examined). It may therefore translate to lower future expenditure, efficiency gains and value for money. This serves as a reminder that expenditure should ultimately be viewed in the context of long-term health outcomes and across budget silos.

Ushering in ‘System Medicine’ with modern data analytics

The term system medicine has been proposed to describe a new paradigm in which the development and selection of treatment strategies for patients with complex diseases is based on data-driven analysis of the human body as a complex system of interacting biological process that determine an individual’s level of health (and disease) (Gietzelt et al., 2016[12]; Tillmann et al., 2015[11]).

ICT allows for combining data from various sources and for analysing large amounts of data to model real-life disease trajectories, disease interactions and effects of medical interventions. Only once patient complexity is better understood can this knowledge be applied in the routine delivery of care for complex patients. For example, clinical decision aids can only provide appropriate and personalised treatment guidelines for patients with multi-morbidity once the interactions of diseases present in the same patient and interactions of corresponding treatments have been studied and are thoroughly understood.

Even if medical research may still be a long way from a true system medicine approach, early examples of how large volumes of electronic data can be used to generate knowledge of disease complexity are already available. The Spanish GMA system (see Chapter 8 on system governance, stewardship and resource allocation) has recently also been used in Catalonia for a number of epidemiologic studies that assessed, for example, the co-morbidity burden, complexity and resource use of patient populations with specific index diseases.4 Such studies help identify patient subgroups that require specific interventions and inform the improvement of their care.

With increased computational power, new techniques can be used to analyse large routine datasets. Data mining, for example, allows medical research to not only take a hypothesis-driven but also a data-driven approach Phinney et al. (2017[13]), for example, show that data mining techniques can identify patient characteristics associated with a high risk of health deterioration simply by recognition of patterns in the data. Results from such analyses can support the delivery of health care but also encourage additional hypothesis-driven research: while algorithmic data mining can, in this example, identify who is at risk of health deterioration, only more traditional hypothesis-driven research can go further and answer the question of why a set of patient characteristics are associated with health deterioration (ibid.). Artificial intelligence can be deployed for making computers more accurate in predicting outcomes, such as hospital readmission, the occurrence of complications or death by feeding them data, and allow for the corresponding adaptation of interventions and care delivery (Topol, 2019[14]; JASON, 2018[15]).

Making the necessary data available to unlock their knowledge potential

Using data analytics for greater personalisation and needs-based redesign of services requires that the necessary data are accessible and available for such purposes. Because of the properties of electronic data, which allow for their duplication and sharing across geographic distances at very low marginal costs, databases to support research can be created relatively cheaply through extraction and linkage of data from routine sources (Kannan et al., 2017[16]). While data extraction, and especially cleaning and curating them for analysis, can be costly, such secondary use is likely much cheaper than original data collection for each research purpose. Registries, for example, can be developed virtually by pooling data from other, existing sources such as health records, prescriptions hospital admissions data. New Zealand has created such a ‘virtual’ national registry of diabetic patients as a useful resource for policy makers, providers and patients (see Chapter 8 on system governance, stewardship and resource allocation).

The combination of data generated in routine health care with other datasets, in particular genomic data, allows for unlocking new knowledge that can help personalise treatments and make them more effective. Agarwala et al. (2018[17]), for example, show how gaps in knowledge underlying the selection of treatments for cancer can be filled by analysis of a combination of longitudinal EHR data from cancer centres with genomic datasets. Using the treatment response and health outcomes achieved in large samples of patients together with information on genetic characteristics of tumour mutations significantly increases the likelihood that the most appropriate treatment combination is selected for any given patient from the plethora of options available (ibid.).

In conclusion, Agarwala et al. (2018[17]) also highlight, however, that accessing dispersed datasets and linking them in accordance with data privacy requirements is very challenging. More integrated information infrastructure and continuous data sharing among providers and laboratories would enable the unlocking of information contained in such data (ibid.). This requires data governance and policy frameworks that manage privacy risk while permitting secondary uses of personal health data for public benefit.

Integration of genomic and other –omics data into care pathway design is an emerging frontier. Geisinger, a private health insurer and integrated provider network in Pennsylvania and New Jersey in the United States, has used data-based patient pathways in the past to reduce unexplained variation in clinical practice and improve the quality of care. Since 2014, Geisinger integrates genomic information into the EHRs of patients who consented to having their DNA sequenced (see Box 2.1). This information is used to personalise care. For patients that are insured with Geisinger Health Plan, clinical exome sequencing is included in benefit packages at no additional cost and any care recommended as a result of a pathogenic variant found in analysis of the sequence is considered medically necessary, and therefore covered per the terms of the individual’s specific benefit package (Williams et al., 2018[18]).

Patients with complex needs benefit from empowerment through information

For patients with complex needs, patient portals are best integrated with a range of tools that help them manage their health and facilitate patient-provider interactions. Because of the co-occurrence of several health problems and the breadth of services they receive, such patients can benefit particularly from more accessible information about their health and health care. A recent literature review from Australia found that successful patient portals are integrated with provider information systems, such as EHRs, and with clinical decision support tools, and provide functions for secure messaging, patient reminders and prescription refill orders (Shaw, Hines and Kielly-Carroll, 2018[24]).

While enhanced information in itself can support self-management through increasing awareness, patient portals are especially effective for complex patients when integrated with self-management applications. They can also be integrated with remote monitoring tools that feed information systems with patient data while patients are not in contact with their health care providers (see Section 2.3.4). Importantly, all these ICT solutions are more effective when part of broader strategies that make care more patient-centred, such as case management (Goldzweig et al., 2013[27]).

Patients who use self-management applications (‘apps’) have been found to perceive greater awareness of their condition, to be better able to make health-related decisions and feel more equal to professionals allowing them to engage in meaningful discussions (Morton et al., 2017[25]). Apps that allow patients to learn interactively, especially through self-assessment and feedback features, increase patient participation in their care (Roberts et al., 2017[26]).

Self-monitoring of data can motivate patients to engage in behaviours that help improve their health outcomes, even when using applications that do not support behaviour change explicitly (Morton et al., 2017[25]). For example, perceiving an interaction between certain activities and physiological data, such as reducing blood pressure by adhering to medication, to better manage diabetes through physical activity and diet, or to control COPD by engaging in more physical exercise, not only encourages further self-monitoring but also motivates to engage in self-management in order to see an improvement in the data (ibid.) This motivation to change behaviour based on physiological data was found even among patients using standalone monitoring systems with no explicit support for behaviour change or educational functions (ibid.).

The Swedish ePATH (electronic Patient Activation in Treatment at Home) project applied a user-centred design process to incorporate a number of ways of enhancing the self-care capacity of patients with chronic or complex diseases (Schildmeijer et al., 2018[28]). In addition to informing patients through functions for planning self-care activities, medication management, health and symptom tracking, and two-way communication with health care providers, the application used various psychological tools to motivate patients to engage in self-care. Through recording self-care activities, health care providers could get a better understanding of symptom development and medication adherence (ibid.).

Technologies that help engage patients in their care are underused

The benefits of giving people access to their own data are many. But recent studies suggest that systems that share and actively provide health data to patients to support self-management are still under-used, in particular for complex patients. A recent analysis of integrated care projects that use ICT for people with multi-morbidity in Europe, for example, found that ICT that supports patient self-management is among the tools least used in these projects (Melchiorre et al., 2018[22]). Barbabella et al. (2017[23]) found that nine in ten hospitals in Europe (90%) do not permit patients to access their own health data. Similarly, in programmes to improve care for multi-morbid patients, tools for sharing of information mainly focus on interactions between professionals and provider organisations, not on making information available to patients (Melchiorre et al., 2018[22]).

For the potential of personalised health information to be realised, patients also have to actively access and used the information that is made available. However, people have sometimes been slow in the uptake of tools such as patient portals (see, for example, NHS Digital (2019[29]) and Adler-Milstein and Longhurst (2019[30])). This underlines the need not only for user-centred design of such solutions but also for supporting people in accessing and making use of the information that is made available.

Although their own data are not yet actively provided to patients in many new models of care delivery, many OECD countries are currently investing in patient portals that make enable people to access their health information and increasingly integrate these systems with the wider health information architecture. Of 15 countries who responded to the survey conducted during research for this report, 12 (80%) reported that they already have or are in the process of implementing an ICT system that gives people access to their own health data.

In Australia, for example, the My Health Record system provides a secure online summary of key personal health information and is available to all residents. Per November 2018, approximately 25% of the Australian population (more than 6 million people) were registered in the system and more than 14 000 health care provider organisation were registered to contribute data, including primary care practices, hospitals, pharmacies, diagnostic imaging labs and pathology practices. In 2019, the Australian government moved towards an opt-out principle to improve uptake of the My Health Record system, so that all residents will have a record by default unless they choose not to have one.

In Canada, the provincial government of Nova Scotia offers its residents a patient portal called MyHealthNS. The portal allows patients and doctors to share information, including routine test results. Once patients have created their secure online health record, they can receive and store test results and specialist reports electronically. They can also log health information, such as blood pressure readings, immunisations, allergies and medications.

All Estonian citizens have access to their electronic health record through a national patient portal using their personal identification number and the relevant security measures tied to it. The portal not only provides access to data but also has a number of basic interactive functions (see Box 2.2).

In Finland, the city of Oulu has opened the Self-Care platform to all of its citizens since 2010. Self-care is a web-based communication platform for patients and professionals that makes available information to encourage healthier life styles and disease prevention and provides support for managing chronic diseases (Lupiañez-Villanueva, Sachinopoulou and Theben, 2015[31]). It is integrated with people’s EHR and provides a wide range of functions, including online booking of appointments and sharing of test results; e-prescriptions; an information portal on treatment of chronic illnesses and health promotion as well as nutrition diaries and weight control tools; an advice service through which people can log inquiries that are answered by health professionals; data governance functions for citizens to authorise data transfers between providers; and a tool for providers to monitor the health status of their patients (ibid.).

While Self-Care is available to all people regardless of their health status, it has been recognised as a key ICT enabler of the chronic care model also implemented by the city, and supports the shared use of data, not only among health care but also health and social care providers. As per 2017, there were approximately 60 000 registered users among a total population of about 200 000 in the city (Oulu Healthcare and Social Welfare, 2018[32]). The goal is to scale the system to the entire region with a population of about 400 000.

Widening health disparities must be actively avoided

Many people with multi-morbidity are likely to adopt and use technologies that allow access to their health information (Yamin et al., 2011[33]). But evidence also suggests that there are disparities in the use of patient portals between patients with different socio-economic backgrounds (Shaw, Hines and Kielly-Carroll, 2018[24]; Goldzweig et al., 2013[27]). These reflect the digital divide and lower digital literacy among disadvantaged population groups (ibid.).

It is therefore important that implementations of patient portals and other ICT that facilitates sharing of data with patients not only make the electronic tools available but also support adoption by people who can benefit most from their use. In Estonia, for example, training courses and tutorials on digital tools are made available to patients and professionals with lower digital literacy. Human centricity and patient empowerment is also among the five pillars of the Estonian e-health strategy (2015-2020), which aims to develop the abilities of people to self-manage and self-educate using apps and online solutions.

Another way of encouraging adoption by patient populations that can benefit from enhanced access to their data is making tools available in provider settings and having professionals demonstrate and support their use (Shaw, Hines and Kielly-Carroll, 2018[24]). In the United States, for example, a mobile device-based patient portal is currently under evaluation that engages patients with multiple chronic conditions during a hospitalisation when one of their diseases deteriorates. This approach is taken because a hospital episode is expected to make the health problem more salient to patients and increase engagement (McAlearney et al., 2016[34]). Using the hospital episode as an entry point, the application then aims to increase patient self-management following discharge through various functions. It provides health summaries, medication listings, daily care plans, health education videos and other materials, advice on prevention, secure messaging with providers and appointment tracking and a patient interface for health data entry. The solution is integrated with EHRs maintained by providers.5

Integrated health record systems are an important foundation

Interoperability and shared data standards or integrated information systems play an important foundational role in enabling this communication and coordination among service providers. Many countries are making good progress in implementing a single, integrated EHR system. In Lithuania, for example, the central e-health system (ESPBI IS) stores patient information from various providers in a single and shared repository, following the principle of ‘One Resident – One EHR’. The system also provides electronic workflows for appointments, referrals and e-prescriptions that save time and reduce errors in transmitting information, making provider interactions more efficient. At the same time, patients can securely access their data online, through a patient portal (a similar patient portal that provides access to EHR data in Estonia is described in Section 2.3.1). Nearly 95% of the Lithuanian population have an EHR and, by mid-2018, more than 70% of providers were connected to the central e-health system.

In the NHS Scotland projects are underway to make electronic records interoperable between the health and social care system, which have historically relied on separate record systems (Gray Steele et al., 2016[20]). This is particularly important for patients with complex needs, who often require health and social care. Time will tell if initiatives such as the ones described here result in better care outcomes and efficiency. Evaluation of initiatives such as these, while challenging, is very important (see Section 2.4.3).

Of course, enabling a range of providers’ access to personal health data introduces risks. Authorisation of access and any alterations to the information must be tracked. This can be enabled by ancillary digital technologies such as a blockchain, which does not hold any health or clinical data but can provide a record of authorisation and access to the data. Estonia, for example, tracks all changes to information on EHRs – including when, where and by whom the entry was made – and keeps a record of all amendments in separate places including on a blockchain. This provides an immutable log should an unauthorised access and manipulation of data occur.

Other ICT functionalities can also contribute

While a shared and interoperable EHR system is a linchpin of care coordination, a wide range of data-driven modalities are available to share information effectively and ultimately improve the people-centeredness and integration of care. A recent review by the RAND Corporation identified five key ICT functionalities that are widely used already or being piloted for care coordination: dashboards, patient relationship management, event alerts, referral tracking and care plans (Rudin et al., 2017[37]). These functions are most effective when integrated with each other and with existing information systems.

Shared electronic care plans, for example, can provide personalised care pathways defined by patient need and outline optimal treatments to both providers and patients. In addition to including shared patient background information, they can include care team member designations that help professionals understand their responsibilities, and task management functionalities that improve treatment adherence (Rotenstein et al., 2016[38]). Similarly, electronic hospital discharge summaries (EDS) can be simple and effective tools to improve coordination of care between hospitals and community-based providers. EDS can be populated and sent automatically from hospital EMR systems and be integrated with reminders for health professionals responsible for post-discharge care.

ICT that allows for remote delivery of services (see Section 2.3.4) can also support interactions among professionals, saving time and making care more efficient. Various ICT-based solutions have been implemented in OECD countries to improve information exchange between professionals to bring medical expertise closer to patients rather than moving patients physically to where expertise is located, especially by linking local providers with specialists based far away.

In Estonia, for example, an e-consultation service has been implemented that allows GPs to consult with specialists on difficult cases online. GPs then either implement specialist advice themselves or refer patients to further services that are necessary. Uptake of the service is incentivised by the Estonian Health Insurance Fund, which pays specialists the same rate for e-consultations as for face-to-face patient contacts. In England, some NHS Clinical Commissioning Groups have implemented virtual services for GPs to send questions to specialists for a quick reply, eliminating the need for specialist appointments. Such services enhance the role of GPs in providing care and frees up specialist time, through avoiding unnecessary referrals. Similar remote consultation services are available to primary care professionals in Canada.

In Poland, a “telestroke” system is being established to increase the speed and therefore effectiveness of treating stroke. The telestroke system uses ICT for remote consultations between specialised stroke centres and local providers where specialists are not available. Project ECHO is an initiative based in the United States developing ICT-based services to support community-based health professionals with remote specialist advice covering a wide range of medical specialties.6 It also allows specialists to learn from cases located far away.

Challenges to deploy ICT relate to engagement and workflow redesign

However, significant barriers remain to greater use of ICT for care coordination. These include, for example, limited engagement of professionals in development and implementation of tools, and attendant challenges with their integration into existing workflows, slow adoption and sub-optimal use, and a lack of standard definitions of the purposes and functions of tools as well as the roles of users (Rudin et al., 2016[39]). Greater user involvement in designing tools is one way to reduce barriers to their adoption (see Section 2.4).

As such, ICT tools that encourage better coordination and integration of care for complex patients are not yet ubiquitous across health systems of OECD countries. Of the 15 countries that responded to the survey conducted in the research for this report, 9 (60%) mentioned initiatives to allow for and/or promote electronic exchange of data between providers. Four countries (27%) reported that patients with multi-morbidity or other types of complexity have been identified as a specific target group for new ways of care delivery and only 2 countries (13%) reported that they are implementing integrated care for such patients. The latter does not necessarily mean that such care delivery models do not exist – but they may not have gone beyond local pilot initiatives yet and may not be a system-level priority.

However, in many OECD countries there are examples of integrated care delivery models that are supported by ICT and the use of data. Many Spanish regions actively enrol complex patients into integrated care. An example of such a model from the Basque Country is in Box 2.3.

In Australia, the Health Care Home (HCH), currently being trialled, aims to provide coordinated and team-based care for patients with chronic and complex conditions, supported by ICT. All patients enrolled in the HCH have an electronic care plan, defined and overseen by a nominated clinician who takes overall responsibility for the care of an individual patient. This electronic plan is shared with patients and all professionals involved in their care. The shared care plan aims not only to increase coordination of the services but also patients’ own participation in their care, both inside and outside of the HCH. Providers are also expected to share patient data and use such data to monitor and track patient health indicators and outcomes (Health Policy Analysis, 2017[40]). A 2-year evaluation of the effects of HCH in terms of quality of care and patient experience, provider experience, health service use and costs is due to be completed by the end of 2019 (ibid.).

Implementing ICT must be part of a broader change and improvement strategy

To truly achieve patient-centred and integrated delivery of health care, however, adoption of ICT that can enhance communication and coordination of care needs to be part of a much broader effort to establish teamwork and collaboration among professionals as a the standard way of operating. This requires not only the right policy framework that encourages cooperation and greater care integration, through institutional structures and incentives, but perhaps nothing short of a fundamental cultural change in the way health professionals are educated and work (Mulvale, Embrett and Razavi, 2016[42]). The workforce considerations of implementing ICT are explored in more detail in Chapter 4.

Examples of projects in OECD countries that aim to improve communication and coordination between providers show that ICT, data and better information can be a key enabler of better collaboration between providers. But instituting the necessary behavioural changes and ensuring that ICT and knowledge are used effectively remains the biggest challenge.

To make care delivery more person-centred and improve the management of chronic disease and multi-morbidity, the Veneto region of Italy introduced Integrated Medical Groups (IMGs) in 2016 as a new model of delivering primary care (Ghiotto et al., 2018[43]). These groups comprise at least 4 general practitioners as well as nurses and other health professionals are embedded in local health units, which promote the integration of health and social care and between hospitals and other medical services, share electronic medical records and provide care in accordance with pre-defined diagnostic-therapeutic pathways (ibid.). These pathways define the respective roles and responsibilities of professionals and how they cooperate among each other; they also aim to promote patient engagement. The extent to which professionals enter and share structured information is monitored.

Germany has historically relied on professional autonomy and office-based physicians in solo-practice as a predominant way of providing outpatient care. Statutory health insurance provides patients with free choice of GPs and specialists and access to care without cost at the point of service. These characteristics pose a challenge in care for chronic diseases. The Joint Federal Committee (G-BA) currently funds the Accountable Care (ACD) project, which uses routine data from sickness funds to identify patients with chronic diseases who see multiple office-based physicians and then aims to improve care coordination among them through moderated working groups (see Box 2.4).

Decision support can be especially useful in complex patients

Patients with complex needs can benefit particularly from decision support algorithms because they often need treatments for several diseases at the same time, which creates complex combinations and risks of adverse interactions. Polypharmacy is often the consequence of having several chronic diseases so that polypharmacy is highly prevalent among older population groups and people with complex health needs. For example, in Sweden a population-based study found that, in 2010, an average adult aged 65 years or above was exposed to 4.6 medicines at the same time (Morin et al., 2018[50]). In this elderly population group, 44% of people were exposed to five or more drugs at the same time, and 12% to ten drugs or more (ibid.)

As described in Section 2.2, treatment guidelines can be contradictory for patients with multi-morbidity because medical research continues to focus on single diseases. Algorithms that match all diagnoses and characteristics of a patient to recommended treatments can help identify possible adverse treatment interactions if clinical guidelines conflict and provide mitigation strategies. To manage complex drug interactions in patients with polypharmacy, algorithms can use electronic data on prescriptions to generate automated warnings of high-risk drug combinations or drug-induced complications to prescribing physicians, pharmacists and patients (Molokhia and Majeed, 2017[51]). Sharing of electronic prescription data can avoid prescribing errors that happen simply because prescribers are not aware of all the drugs taken by their patients (Lavan, Gallagher and O’Mahony, 2016[52]).

Electronic drug monitoring tools can also make health care more efficient by freeing up time spent by professionals on tasks that can be automated, without compromising outcomes. A study of the work of nurses in an Australian hospitals, for example, found that nurses spent significantly less time on medication monitoring tasks and more on other patient care-related tasks following the implementation of an electronic monitoring tool for rheumatology patients (Callen et al., 2013[53]).

Ensuring patient involvement in decision making

Patient involvement in decision-making is an important part of patient-centred care. Algorithms can also be designed to take into account patient preferences (Wilk et al., 2017[54]; Zamborlini et al., 2016[55]). However, patient involvement is still the exception rather than the rule. A literature review published in 2015 found that patient participation in making decisions is still limited and that few tools are developed to actively and directly involve patients in the decision process (Sacchi et al., 2015[56]). Similarly, a review that focused on decision aids in the care for patients with multi-morbidity also found that patients were often not actively involved in the decision-making processes (Fraccaro et al., 2015[57]).

Integration and interoperability are of vital importance

Interoperability of information systems and data quality are key to realising the full potential of data-based decision aids. Algorithms can only produce accurate and personalised care recommendations if underlying data are accurate and contain the necessary variables to provide complete personal profiles of patients. This requires combining personal data from disparate sources. However, many decision aids currently used do not yet take a system medicine approach to integrate data from various sources. For example, Gietzelt et al. (2016[12]) found that many decision models only use a single type or source of data and few combine more than two types or sources of data. Decision models that do combine at least two types of data most commonly use genomics and molecular data combined with clinical data extracted from electronic medical records (ibid.).

Data from EHRs are also used for predictive modelling to improve the appropriateness of care as patients receive treatment. For example, EHRs have been used at Kaiser Permanente in the United States to predict deterioration of hospital inpatients and unplanned transfer to intensive care units (Kipnis et al., 2016[58]). In another study using EHR data from Canadian and United States university hospitals, deep learning algorithms achieved high accuracy in predicting disease onset, hospital mortality, unplanned readmissions, prolonged length of stay and the final discharge diagnoses of patients (Rajkomar et al., 2018[59]; Miotto et al., 2016[60]).

Decision support systems are best integrated with other ICT tools that support the delivery of care and embedded into clinical workflows. In the United States, emergency room software that unifies all relevant patient information and integrates it with checklists and decision-support was found to reduce mortality and length of stay, resulting in cost savings (Olchanski et al., 2017[61]). A recent review from Australia found that decision-support systems are most successful when implemented in combination with additional software components and that their adoption, and ultimate effect on the quality of care, can be improved by ensuring interoperability with existing ICT systems and focus on a local minimum set of indicators (Shaw, Hines and Kielly-Carroll, 2018[24]). Integration with other ICT systems is also key to understand the entire range of information various systems deliver to health professionals and patients, to help ensure that people are not overloaded with information and risk ignoring the most important alerts that decision support systems deliver.

More research is needed to establish the effectiveness of decision aids

Although the utility of decision aids may be more straightforward to evaluate than other ICT tools (through independent validation of the appropriateness of recommendations the systems generate) evidence of decision aids’ effectiveness remains patchy. A review by Bennett and Hardiker (2016[48]) concluded that there was mainly low-quality evidence of the effectiveness of decision aids used in emergency care. Looking at decision aids in the care for patients with multi-morbidity, a review by Fraccaro et al. (2015[57]) found that there were no rigorous evaluations of usability or effectiveness of the tools used.

Tele-monitoring can make care more responsive and appropriate, leading to increased effectiveness and efficiency

Tele-monitoring can increase the awareness of patients’ conditions by health professionals and enable earlier and more accurate identification of clinically relevant symptoms, signs and signals through electronic transmission of patient data – in most cases passively (without the patient having to manually input data). This can make health care more responsive and appropriate, improve therapy and medication adherence, avoiding costly interventions later on. Providing professionals with real-time data on their patients may help make face-to-face interactions more timely and focused, and increase patient adherence to treatments, ultimately making treatments more effective (Morton et al., 2017[25]; Noah et al., 2018[63]). By focusing the time of professionals on the most important tasks, tele-monitoring can also increase productivity (Noah et al., 2018[63]).

All of this can particularly benefit patients with chronic diseases, who need treatments over prolonged periods of time and specific acute-care interventions when their conditions deteriorate, and people who live in remote and underserved areas. At the same time, the abundance of data generated by tele-monitoring tools can pose privacy risks, may cause information overload and may lead to unrealistic expectations of patients vis-à-vis health professionals.

A trial in psychiatric care in the United Kingdom, for example, found that providing regular feedback to therapists on patient outcomes allowed therapists to focus their attention on patients who were not on track and to identify and resolve obstacles to clinical improvement, which ultimately alleviated depression and anxiety (Delgadillo et al., 2018[64]). Outcome feedback in this context refers to routine monitoring of the patient’s condition and comparing the patient’s symptoms with those observed in similar cases (Delgadillo et al., 2018[64]; Glazebrook and Davies, 2018[65]).

While many individual examples of the use of tele-monitoring exist in OECD countries, few countries use them on a large scale to improve care. Of the 15 countries that responded to the survey conducted in research for this report, only 4 countries (Canada, Israel, Norway and Poland) mentioned projects to implement patient tele-monitoring systems. Analysis of the WHO Third Global Survey on eHealth (conducted in 2015) found that only Canada, Japan and Spain already had well established and relatively large-scale tele-monitoring systems (Cravo Oliveira Hashiguchi, forthcoming[62]). In Canada, for example, tele-monitoring has been promoted as a tool for patients with complex chronic diseases to improve self-management of their illnesses. Canada Health Infoway, the national funding entity that promotes health-related ICT, has made tele-monitoring a priority and funded a number of projects across provinces, including the Ontario Telehomecare project (see Box 2.6). In many other countries, tele-monitoring is often only used in small-scale pilot projects (Cravo Oliveira Hashiguchi, forthcoming[62]).

For complex patients, tele-monitoring can be integrated with ICT tools that support patient-self management. Giving patients access to their own data has a number of advantages in itself and the data captured by tele-monitoring tools can also drive personalised supports for self-management, such as reminders, goal-setting or personalised life style advice (see Section 2.3.1). Tele-monitoring tools are powerful tools to improve medication adherence, by enhancing, for example, patient education and patient awareness of their own medication-taking patterns (Vrijens, Urquhart and White, 2014[71]). A randomised pilot study of a tele-medication monitoring system in the United States, for example, showed that such a system can reduce the number of hospitalisations and length of inpatient stays of patients with chronic heart failure (Hale et al., 2016[72]).

Potential risks of tele-monitoring must be recognised and managed

There are a number of pitfalls to avoid when implementing patient tele-monitoring tools. Most importantly, people need to be comfortable with sharing their data with professionals through ICT tools. A poll in the United Kingdom found that 57% of respondents were willing to share data with the National Health Service via a lifestyle app or fitness tracker (Castle-Clarke, 2018[73]). Adoption of tele-monitoring tools may be more difficult to achieve among elderly patients, who are more likely to have complex health needs. Results from the poll in the United Kingdom indicated a clear generational gradient of respondents: while approximately 70% of 15-24 year olds were “very or fairly willing” to share such data and only 25% in that age group were “very or fairly unwilling” to do so, just under half of over 65 year-olds were willing to share data with the other half unwilling to do so (ibid.).

To build trust and acceptance of tele-monitoring, ensuring data privacy is paramount. While data privacy and security are broader concerns related to all technologies that transmit personal health data electronically, not all technologies currently available are secure. Especially applications that can be downloaded by patients themselves and are not subject to regulatory oversight may bear risks. A review by Dehling et al. (2015[74]) of more than 24 000 mobile health apps available for Apple iOS and Android, for example, found that more than 90% of apps available posed at least some risk of damage through information security and privacy infringements while some 12% of apps were classified in the highest risk category.

Professionals can be burdened by unrealistic expectations of continuous monitoring (Morton et al., 2017[25]). While the feeling of being monitored, in particular when patients are contacted by professionals when the monitored parameters are out of range, can reduce anxiety by patients, it can also induce feelings of over-reliance on professionals. Where the level of patient autonomy permits, one solution to these problems is to make patients responsible for contacting professionals when their data were not within an expected range, which can improve both, patient empowerment and the quality of care by making interactions more effective. The style of feedback has an important influence on how much responsibility the patient adopts for self-management (ibid.).

There may also be emotional barriers to adoption of remote- and self-monitoring tools. A 2015 study from the United States, for example, investigated the perceptions of electronic health monitoring tools by multi-morbid patients (Ancker et al., 2015[75]). It found that patients sometimes perceived monitoring data as an additional burden, that making data more salient to patients can provoke strong emotional reactions and that patients often notice that physicians have more trust in data that is measured by technology than in self-reported information (ibid.).

Evidence is encouraging but difficult to generalise

As with other ICT solutions that have the potential to improve care, rigorous evidence of the effectiveness of tele-monitoring in terms of health care process measures and health outcomes is only just emerging. Because tele-monitoring can be used in many different ways, studies are often context-specific and their findings cannot be generalised easily. Also, studies of effectiveness in terms of health outcomes often only look at patients with specific diseases, and not necessarily the most complex patients with multiple health problems.

A recent OECD working paper found that tele-monitoring improves patient satisfaction, empowerment and reassurance by providing a greater sense of security while away from health care providers. Tele-monitoring has also been found to reduce emergency room visits and unplanned and avoidable admissions to hospitals by following patients more closely in their own homes while it appears to either have no effect or increase the use of face-to-face primary care (Cravo Oliveira Hashiguchi, forthcoming[62]). Increased use of face-to-face care, whether appropriate or not, is often a result of greater patient awareness of medical needs (ibid.). Where this leads to more appropriate care, tele-monitoring can also improve health outcomes. It has been shown, for instance, to reduce mortality of patients with heart failure (HF). A recent literature review by Noah et al (2018[63]) found that remote monitoring tools had positive early effects in terms of clinical outcomes in the management of some chronic diseases, including COPD, Parkinson’s, hypertension and lower back pain. On the other hand, integrated self-management tools were not always effective. The review focused on non-invasive, wearable devices that automatically transmit data to a web portal or mobile application for the purposes of self-monitoring or monitoring by a health professional.

Remote delivery of services can improve access and make care more efficient

Delivery of services across geographic distances using ICT can save time for health care providers and patients and improve access to services in remote areas and for isolated sub-populations. Remote service delivery can thus make health care more efficient and more equitable. However, equitable access to enabling technologies and special support for people with lower digital and health literary are prerequisites for achieving the goal of equity. Policy also needs to ensure that the ease of accessing remote services does not lead to additional demand by population groups with better digital literacy, who also tend to be younger and healthier, at the expense of those in need.

Similar to tele-monitoring, the growing evidence base on remote service delivery needs to be interpreted carefully as ICT can facilitate the delivery of appropriate and inappropriate services alike. Evidence suggests that services delivered remotely result in health outcomes that are comparable to outcomes of care delivered face-to-face, while there are a number of non-clinical benefits to patients, in particular ease of access but also increased timeliness, coordination and continuity of care and promotion of knowledge sharing and continuous learning among professionals and patients (Cravo Oliveira Hashiguchi, forthcoming[62]).

Tele-medicine often complements, rather than replaces, face-to-face care

While remote delivery of services cannot always replace face-to-face consultations, remote interactions with professionals can nevertheless serve as an efficient entry-level contact with the health system, and improve the patient-centeredness, appropriateness and ultimately the effectiveness of subsequent face-to-face services (Pecina and North, 2016[76]). For people with multiple chronic diseases who require care over prolonged periods of time, remote delivery of care can greatly enhance access to appropriate services, in particular for people with limited ability and those living in areas that are remote or have poor provider infrastructure. Remote service delivery is best integrated with remote monitoring solutions described above.

In Australia, for example, Head to Health is a digital mental health gateway that aims to improve access to mental health services most suited to peoples’ needs through a stepped-care approach supported by ICT. Services can be accessed through a single webpage,8 which either makes electronic services available directly, allows people to access remote telephone and online crisis counselling and to schedule face-to-face consultations with professionals. Digital services generally focus on highly prevalent conditions, such as anxiety and depression, and are delivered via desktop computers and mobile apps.

For people with multiple chronic diseases who require care over prolonged periods of time, remote delivery of care can greatly enhance access to appropriate services, in particular for people with limited mobility and those who live in areas that are geographically remote or have poor provider infrastructure. This requires that remote monitoring solutions described above are integrated with remote service delivery and that information generated by remote monitoring leads to appropriate patient/provider interactions. The review by Melchiorre et al. (2018[22]), however, found that electronic tools for remote monitoring and patient/provider interactions are not yet widely used in care delivery for patients with multi-morbidity. Impacts on costs and efficiency need to be monitored and evaluated carefully.

The effects of remote service delivery on total costs and efficiency are not easy to predict. Where remote consultations replace and avoid unnecessary, face-to-face contacts and help avoid unnecessary face-to-face consultations, they can lead to cost savings or efficiency gains. By providing an easy first point of contact with the health system and making services more accessible, however, they can also increase demand for both, remote and face-to-face provider consultations and increase costs (Castle-Clarke, 2018[73]). Evidence on cost-effectiveness of care delivered through tele-medicine is context-specific and cannot be easily generalised with the data that are reported (Cravo Oliveira Hashiguchi, forthcoming[62]). As illustrated in the example of the UK Personalised Integrated Care Programme (Section 2.2.1), additional demand may also represent previously undiscovered health need, leading to better outcomes (and perhaps reduced long-term costs).

In another example, an initial evaluation of the “GP at Hand” primary care practice in London found that patient registration with the service coincided with declines in the use of emergency services, suggesting that there may be some efficiency gains from replacing costly hospital services. At the same time, the rapid uptake of services by people who are relatively young and healthy suggests issues with financial sustainability if the care delivery model were to be scaled. The initial evaluation of GP at Hand also indicated that people preferred remote consultations with physicians over automated services (Ipsos MORI et al., 2019[77]), suggesting that efficiency gains by substituting human resources may be difficult to achieve. Further information on GP at Hand is in Box 2.7.

Ensuring equity is a challenge

Equitable access to enabling technologies and special support for people with lower digital and health literary are key prerequisites for tele-medicine to achieve its goals of increasing equity and efficiency. In addition, patients need to trust that the data transmitted by digital technologies are safe. These prerequisites are not always met in existing examples of care delivery models that feature tele-health technologies.

In many OECD countries, broadband internet access is more common in households in urban areas than in rural areas and in households with higher incomes (OECD, 2019[78]) (also see Section 2.4.2). The Gesundes Kinzigtal program in Germany (see Chapter 8 on system governance, stewardship and resource allocation for details), for example, struggled with insufficient IT infrastructure in remote areas and a large proportion of the population targeted, especially elderly people, were reluctant to adopt technologies, also due to lack of trust in data security (Melchiorre et al., 2018[22]). The review by Castle-Clarke (2018[73]) of ICT used in health care in the United Kingdom, including technology for remote delivery of services, found that people have limited knowledge of how data collected through ICT are used by the NHS and other organisations, which may be a cause of mistrust.

Unequal access to and use of ICT can lead to services be taken up by people with lower need, causing a misalignment between need and resource allocation. In England, for example, providers increasingly offer remote primary care services. GP at Hand (see Box 2.7) is mainly used by young and well-educated professionals but few patients with complex needs. Critics argue that use by mainly healthy and low-risk populations may divert resources away from people with higher need (DigitalHealth, 2018[79]; Oliver, 2019[80]; Iacobucci, 2018[81]), causing negative effects on efficiency and equity.

New models of care delivery also require new payment mechanisms

New ways of delivering care, by definition, require new processes and workflows. A recurrent barrier to scaling these innovations is insufficient funding or financial disincentives for adopting the new way of working. To overcome this, provider payment mechanisms need to be aligned to encourage care coordination and the use of supporting ICT. This requires a move away from fee-for-service (FFS) payments.

Alternative provider payment mechanisms, such as bundling, capitation and pay-for-performance, can play an important role in facilitating the adoption of new ways of delivering care (see Chapter 8 on system governance, stewardship and resource allocation for further discussion on provider payment). In Australia, for example, the Health Care Home (HCH) for patients with chronic and complex conditions described above deliberately deploys bundled payments instead of FFS (the conventional provider remuneration mechanism in Australia). Participating primary care practices that take overall responsibility of a patient’s care receive a monthly payment per patient to cover all care related to the chronic condition(s), including planning and review, and coordination of care (Health Policy Analysis, 2017[40]).

However, funding also needs to recognise up-front costs of designing and implementing ICT, and provide incentives or direct investment for implementing ICT tools that can increase effectiveness and efficiency of services at the margin. Up-front costs for designing and implementing ICT tools that support innovative care delivery are typically high while marginal costs of using them are usually low (sometimes approaching zero). For example, purchasing software might incur a one-off cost or annual license fee irrespective of whether it is used with 1, 10 or 1 000 patients. Additional funding may be necessary to cover initial implementation costs and relieve providers of some financial risk related to such investments. Sufficient upfront funding for new ICT tools can encourage innovation and finance necessary training and support for professionals and patients to facilitate implementation (Melchiorre et al., 2018[22]).

In scaling the GMA tool in Spain, the national Ministry of Health, Consumer Affairs and Social Wellbeing (MSCBS) funded the initial implementation of the tool in the various Spanish regions that manage their health systems autonomously. Ongoing costs of operating and using the tool are borne by regional health authorities.

In Canada, new care delivery models and services that rely on ICT are overseen and funded by Canada Health Infoway (also referred to as Infoway), an independent, not-for-profit organisation created and funded by the federal government. Infoway acts as a strategic investor, funding projects with provincial and territorial governments on a shared-cost basis, typically on a 75:25 ratio. Requests for proposals by provincial and territorial health departments adhere to defined criteria and milestones and all projects that receive Infoway funding are subject to an independent evaluation. Infoway has also directly designed, developed and implemented new ICT services, with 100% federal funding (such as PrescribeIT, an e-prescription system).

Interoperability and shared infrastructure enable scale-up of new ways of delivering care

Inadequate ICT infrastructures and limited interoperability of various tools are a common barriers to better integration of existing health care services (Melchiorre et al., 2018[22]). For example, among 101 innovative models of care for multi-morbid patients in Europe that use at least one ICT tool reviewed by Barbabella et al. (2017[23]), the scale of more than three-fourths remained local or regional and only about half were integrated into the wider health system.

Locally developed ways of care delivery and supporting ICT tools that are not interoperable with existing ICT infrastructures risk fragmenting care further rather than help integrate it and will also hamper scale-up of new ways of working beyond the local context. ICT tools that are developed in isolation for individual diseases also pose similar risks of further fragmentation of care for multi-morbid patients. Regulation and project funding mechanisms can set requirements for new ICT tools with respect to data standardisation, interoperability with other tools and their suitability for existing ICT infrastructures. These levers are typically best embedded in a national data governance and policy framework.

Skills need to evolve and cultural change be managed

Another significant issue pointed out in previous studies of ICT-supported care delivery is the lack of ICT skills among patients and professionals (Melchiorre et al., 2018[22]). Cultural factors, such as general resistance to change or professional autonomy, can also hamper adoption of new tools and new ways of working. User involvement and designing new care pathways and tools that do not add to the workload of professionals and self-care burden of patients are one way of reducing cultural resistance. Their involvement can also help create a sense of ownership and ultimately encourage uptake of the solutions.

In addition to a data governance framework that ensures data privacy, new tools also need to be accompanied by appropriate training programs, technical support and change management processes, in particular for health care professionals. While skilled professionals can lead the way in making patients more comfortable with new processes and technology, a lack of skills may reinforce cultural resistance to using ICT tools as a routine way of working (ibid., also see Chapter 4 on the health workforce).

Policy should tackle persistent disparities in digital and health literacy

With nearly ubiquitous availability of mobile devices and internet connectivity, the digital divide may be ostensibly narrowing, but inequalities in internet access persist. On average across OECD countries, the number of mobile broadband subscriptions is close to the number of people in the population (OECD, 2019[83]). However, especially in OECD countries with lower incomes but also in some high-income countries such as Belgium, Germany and Luxembourg, there are fewer subscriptions than people in the population (ibid.). Broadband access is more common among households in urban areas and with higher incomes (OECD, 2019[78]).

Significant inequalities in digital skills are also observed. As internet connectivity improves, related factors that inhibit adoption of ICT among high-need populations may gain in importance. Even if they are connected, population groups with high health need may still be disadvantaged in terms of their capacity to use ICT. People aged 55-74 were less likely than those aged 16 to 24 to use the internet in every OECD country for which data were available in 2016 (OECD, 2019[78]). Data on adult competences suggest that on average, 32% of those aged 55-65 have no computer experience or have failed core ICT tests, compared with just 5% of 16-24 year-olds (OECD, 2017[84]). The King’s Fund reports that about one-fifth of the United Kingdom population lack basic digital skills, in particular people in lower socio-economic groups (Castle-Clarke, 2018[73]). Figure 2.1 shows inequalities in the diffusion of online activities between people with high and low education levels in the population and between OECD countries.

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Figure 2.1. Socio-economic disparities in online activities

Diffusion of selected online activities among individuals aged 16-74 in OECD countries, 2018.

Source: OECD ICT Access and Usage by Households and Individuals database (OECD, 2019[78]).

Poor digital literacy among high-need populations is one of the factors that impede the effective use of health-related ICT. A recent literature review on adoption of health-related applications in typically underserved populations,9 for example, found that the main barriers to adoption were low health literacy and lack of experience with using ICT; difficulties in accepting the presented information, for example, because it was considered not useful, confusing or contradictory to users’ own experience; and user-unfriendly and poorly designed interfaces (Huh et al., 2018[85]).

Foundational skills related to digital technology but also health literacy in all population groups, and in particular among the most vulnerable, are a key prerequisite for ICT-enabled care delivery to meet its goals. This is particularly true for care delivery supported by patient-interactive ICT, such as patient portals, remote monitoring or self-care devices. More broadly, further investments are needed to develop skills related to digital technologies and health literacy. These include offering incentives for and easing access to adult learning and improving the recognition of skills acquired after initial education so that everyone can participate in a digital society. Of course, more ‘upstream’ interventions such as social policies that support mobility and redistribution can also reduce digital divides.

Digital literacy is promoted by broader policies that aim to help people benefit equitably from an increasingly digitised economy. The Framework for Policy Action on Inclusive Growth (OECD, 2018[86]), which is part of the OECD Inclusive Growth Initiative, aims to help governments ensure a more equitable distribution of the benefits from economic growth along three major axes:

• Investing in people and places that have been left behind, which highlights the promotion of life-long learning and the acquisition of skills, increasing social mobility, improving health and enhancing access to affordable housing, promoting regional catch-up and investing in community well-being. This requires ongoing financial commitment.

• Supporting business dynamism and inclusive labour markets, which underscores the need to improve technology diffusion, innovation and entrepreneurship, as well as resilient labour markets and good jobs for all.

• Building efficient and responsive governments, which advocates for good governance and people-centred digital government strategies, as well as a whole-of-government approach to policy development and implementation.

Routine health data should be deployed to evaluate care delivery

Rigorous methods, such as cluster-randomised controlled trials or case-control studies that rely on routine health care data, can be used to evaluate new ways of delivering care and supporting ICT tools in terms of their effects on process-related or health outcome measures. Where possible, evaluations should be conducted by independent parties who do not have a vested interest in the success of a new care delivery model or ICT tool. This can help avoid bias, and reduce the risk of non-publication of negative findings.

Evidence on ICT-enabled care delivery is currently building. But the evidence base in terms of health outcomes is still limited in several fields of application of ICT and cannot be generalised easily, for instance in remote patient monitoring (see Section 2.3.4 and Noah et al. (2018[63])). Evaluation methods are not always rigorous and pre-post studies without control groups are common. Rigorous evidence is also scarce on projects that make secondary use of clinical data to generate knowledge for improving health care. A recent literature review concluded that many studies report how secondary use of data should impact care processes, health outcomes, productivity and costs rather than actual effects (Meystre et al., 2017[88]). Pilot studies of health-related ICT tools have often yielded little evidence to guide further implementation and scale-up of these technologies (Wilson et al., 2018[89]). Where evidence is available, it can be difficult to interpret and to use for decision-making because of varying terminology, design of interventions and rapidly evolving technology (Shaw, Hines and Kielly-Carroll, 2018[24]).

Qualitative process evaluation can complement quantitative studies of effectiveness. Process evaluations can help, for example, distinguish reasons for failure of achievement of desirable outcomes between: (1) implementation failure or (2) the failure of the intervention itself (Maar et al., 2017[90]), which is particularly helpful in learning from failures of complex interventions that may fail for a variety of reasons. Identifying the main factors that caused success or failure can also help make adjustments to care delivery and ICT tools. This is particularly important in the development of ICT tools that are user friendly and support person-centred health care, which requires iterative approaches.

It is therefore key that health systems continue to evaluate new solutions and that evaluations be embedded in project implementations. Where possible, evaluations should make secondary use of existing data to produce results quickly and cheaply (also see Chapter 7 on biomedical technology). They should combine rigorous quantitative methods to assess effectiveness with qualitative research to explore the reasons for the results observed and help make adjustments to new ways of working and supporting ICT tools. Doing this well requires building (and investing in) the necessary technical and policy capacity.

A fit-for-purpose approval and regulation model may be needed

For health-related ICT tools, which can be developed in iterative processes that allow for changes and improvements to be made as soon as deficiencies become apparent, different evaluation methodologies might be needed than for medicines or medical devices. An agile and user-centred research and development cycles have been proposed to adapt the current 4-phase regulatory approval process for medicines and also allow for continuous and iterative development and incorporates development and testing processes typically used for ICT (Mathews et al., 2019[91]; Wilson et al., 2018[89]). Importantly, this approach would allow for limited market releases of ICT tools that are proven safe to test their effectiveness while allowing for replacement of existing versions by subsequent iterations as these become available (ibid.).

The US Food and Drug Administration (FDA) is currently implementing its Digital Health Innovation Action Plan. Under the plan, the regulatory agency is formulating new standards for mobile medical apps, tele-medicine and software as a medical device (SaMD), among other digital health technologies, to reduce delays in access to lower-risk technologies while ensuring safety and effectiveness of regulated technology.