Extended abstract

A comparison of four models

Use of patient-generated mHealth data for patient care

DOI: https://doi.org/10.4414/smi.32.00357
Publication Date: 21.10.2016

Frederic Ehrlera, Christian Lovisa, Katherine Blondonb

a Division of medical information science, University Hospitals of Geneva, Switzerland
b Medical directorate, University Hospitals of Geneva, Switzerland


The rapid adoption of mobile applications for wellness and health tracking has resulted in vast amounts of patient-generated data. However, these data are often underutilised in traditional patient care. In this paper, we explore how to use these patient-generated data to improve patient care. Based on a review of healthcare models and recommendations, we proposed and compared four models with increasing integration with electronic health records (EHRs). We also compared the freedom of choice of apps, as well as content validity and expected effectiveness. In the first model, patients have the full range of app choice, and full control over their data, in particular for sharing with healthcare providers. In the second model, patients use a selection of apps to export their data to a repository, which can be accessed by their providers (without integration into the EHR). In the third model, interoperability between the apps and the EHR allows full integration, but restricts app choice. Finally, the last model adds the notion of cost-effectiveness to the previous model. Although the EHR-integrated models limit app choice for patients, the app content is medically validated and patient-generated data are more easily accessed to improve patient care. However, these integrated models require decision-support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care.

Key words: mHealth; patient empowerment; quantified self; healthcare delivery system; interoperability


The rapid growth of mHealth has led to vast amounts of patient-generated data through mobile applications and trackers for wellness and health. Indeed, mHealth provides new and easier ways to track, visualise and share data, through reminders, prompts and social media [1]. Despite their potential value to improved patient care, patient-generated data are often underused [2, 3]. Reasons for this underutilisation include high volume of produced data, lack of connectivity and interoperability with electronic health records (EHRs), as well as concerns about the validity of app content and tracking devices. Indeed, the majority of apps and tracking devices have low regulation standards by the US federal authorities [4], in part to support the development and innovations of new apps. The rapid turnover of available apps makes it difficult for healthcare providers to keep up-to-date with the patient apps, even within their medical specialty. As a result, patients depend on their own searches or on advice from peers to find suitable apps for self-management [5].

In this paper, we propose and compare four models of ways to use patient-generated data for patient care, emphasising the advantages and disadvantages of each model.


Based on a review of current healthcare delivery models, health behaviour theories and recommendations, we propose four models offering different levels of integration of patient-generated data into existing medical documentation and workflow. We compare patient satisfaction in terms of guidance and choice of apps, as well as validity and expected effectiveness of health apps in each model.


We present four models with increasing EHR integration, and a comparison of these models in table 1.

Model 1: patient engagement (patient-driven) model

Patients are empowered by mobile devices to collect self-reported data. They choose their apps freely, manage their own data, and decide what and whom they share their data with. This model engages patients to manage their own health, and relies on prior knowledge, health literacy and validity of existing apps.

Model 2: partially integrated patient-generated data model

In this model, the patient collects data from a subset of apps, chosen for exportability of results. These results are centralised in a patient portal for easy retrieval, and can be shared with chosen healthcare providers from an organisation. These providers can visualise the data, but the data are not integrated with the rest of the medical record.

Model 3: fully integrated patient-generated data model

This model provides a more restricted choice of apps for data entry, but allows all collected data to be imported and integrated into the EHR. Healthcare providers can therefore see outpatient results integrated longitudinally with the inpatient results, for example, without additional navigation in the chart. Patients can access their data, and may choose the shared parameters, but do not necessarily have access to the entire EHR data.

Model 4: integrated healthcare delivery and payment model

In this model, the notion of cost-effectiveness is added to the previous model. By taking into consideration healthcare costs, integrated healthcare institutions (insurance and care delivery by the same entity) can promote patient engagement and population health.

Table 1: Comparison of the four patient-generated data integration models.
ModelStructured dataApp choiceApp validityData qualityData managementIntegration in care
2Low FreeLowLowPatientLow
4HighVery restrictedHighHighInsuranceHigh


Model 1 places the patient at the centre of care, as recently recommended by the World Health Organization to address fragmentation of care. The empowered patient is therefore a member of an interprofessional collaboration team [6]. In this model, interoperability requirements for the healthcare providers are low. The patients can select the relevant data that they want to share, and show the results to their doctors, or send them via email, for example. However, the patient may not receive any guidance about which apps to use, nor app validity. This is actually quite common in self-management of chronic diseases such as diabetes. Advice from peers with patient expertise may provide better support in this aspect [7].

In model 2, patient satisfaction with app choice may be lower than in model 1, since data need to be exportable to a patient portal. Via the portal, the patient-generated data are accessible to the chosen providers, allowing them to consider a part or all of the data for patient care. The drawback is that the patient data are kept separate from the rest of the EHR data, which may discourage use of that data for patient care.

In model 3, interoperability requirements limit the choice of apps for patients, but allow easier access to patient-generated data. Access to these data, however, does not necessarily mean that it will be used to improve patient care. One advantage of a restricted app choice is higher app validity, since medical liability requires healthcare providers to recommend only validated apps. The risk in this model is for the providers to be swamped with large amounts of patient-generated data, with potential liability issues if abnormal results go unnoticed. This model should therefore also include an efficient decision support system with notifications and alerts for providers to identify abnormal results or trends.

For model 4, healthcare costs are a driver for quality of care: the goal is to improve patient engagement by supporting apps for data collection, as well as to use this data to improve patient care. Integrated institutions can invest more in health promotion and patient empowerment, with a higher return on investment, even potentially for more distal outcomes. Investing in health promotion can lead to the development of smarter apps, or apps to support more complex health conditions. In the long run, developing a system that can support the healthier, younger or more engaged population may allow a shift in cost towards more in-person care for the rest of the population. The required infrastructure for this model is less common, and needs to be built cautiously, to avoid repercussions of suboptimal patient outcomes on access or quality of care.


Although our EHR-integrated models restrict the choice of apps for patients, they would have the advantage of using validated apps, some guidance from providers, and have a better chance of receiving care that uses the content of the patient-generated data. However, these integrated apps require decision support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care. Future directions include exploring the providers’ acceptability and the feasibility of using patient-generated data for patient care.

Disclosure statement

The authors are supported by the University Hospitals of Geneva.


Frederic Ehrler
4, rue Gabrielle-Perret-Gentil
CH-1205 Genève


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