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SMART Markers: collecting patient-generated health data as a standardized property of health information technology.

ABSTRACT: A patient-centered health system needs precise computable measurements to derive value. While validated patient-reported outcomes (PROs) are increasingly used in trials, their adoption in care remains limited and generally separated from the medical record. Further, absence of systematic processes for patient-led data submission excludes valuable data from digital devices that can potentially aid in contextualizing health status. With prior experience in developing apps for the Patient-Reported Outcomes Measurement Information System (PROMIS), we sought to make collecting patient-generated health data (PGHD) a fundamental property of health information technology at scale, and in an interoperable, standards-compliant fashion. We build upon the open SMART on FHIR (Fast Health Interoperability Resources) specification to create SMART Markers-a mobile device software framework encapsulating functionality needed for rapid deployment of both patient- and practitioner-facing PGHD apps. We refactored previously developed PROMIS apps to use SMART Markers for handling PGHD-request creation, on-device administration, and generation of a variety of PGHD types and submission of results to a FHIR server. Validation and conformance tests were performed on the generated output and app-reusability was demonstrated across two demo servers. App developers can import SMART Markers into their existing or new apps to readily leverage an interoperable PGHD capturing functionality out of the box, without having to reinvent the wheel. Our approach enables the creation of health system integrated, context-specific experiences for both patients and practitioners.

SUBMITTER: Sayeed R

PROVIDER: S-EPMC6978521 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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SMART Markers: collecting patient-generated health data as a standardized property of health information technology.

Sayeed Raheel R Gottlieb Daniel D Mandl Kenneth D KD

NPJ digital medicine 20200123

A patient-centered health system needs precise computable measurements to derive value. While validated patient-reported outcomes (PROs) are increasingly used in trials, their adoption in care remains limited and generally separated from the medical record. Further, absence of systematic processes for patient-led data submission excludes valuable data from digital devices that can potentially aid in contextualizing health status. With prior experience in developing apps for the Patient-Reported ...[more]

PMID: 31993507

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Project description:IntroductionThis article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. "Smart Medical Information Technology for Healthcare (SMITH)" is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH's goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach.ObjectivesTo give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented.Governance and policiesSMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals' Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage.Methodology and architectural frameworkTo share medical and research data, SMITH's information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM2 for enterprise architecture modeling supports a consistent development process.The DIC reference architecture determines the services, applications and the standardsbased communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability.Use casesThe methodological use case "Phenotype Pipeline" (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case "Algorithmic Surveillance of ICU Patients" (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a "hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections" (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design.DiscussionSMITH's strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals' information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

Project description:BACKGROUND:Unhealthy dietary behaviour-high intake of fat, sugar, and salt, and low intake of fruits and vegetables-is a major risk factor for chronic diseases. There is a lack of evidence-based interventions to promote healthy dietary intake among Indian populations. Therefore, we tested the effectiveness of an information technology-enabled 'SMART Eating' intervention to reduce the intake of fat, sugar and salt, and to increase the intake of fruits and vegetables. METHODS:In Chandigarh, a North Indian city, a cluster randomized controlled trial was implemented in twelve geographical clusters, based on the type of housing (i.e., LIG: Low-income group; MIG; Middle-income group, and HIG: High-income group-a proxy for socio-economic status). Computer-generated randomization was used to allocate clusters to intervention and comparison arms after pairing on the basis of socioeconomic status and geographical distance between clusters. The sample size was 366 families per arm (N = 732). One adult per family was randomly selected as an index case to measure the change in the outcomes. For behaviour change, a multi-channel communication approach was used, which included information technology-short message service (SMS), email, social networking app and 'SMART Eating' website, and interpersonal communication along with distribution of a 'SMART Eating' kit-kitchen calendar, dining table mat, and measuring spoons. The intervention was implemented at the family level over a period of six months. The comparison group received pamphlets on nutrition education. Outcome measurements were made at 0 and 6 months post-intervention at the individual level. Primary outcomes were changes in mean dietary intakes of fat, sugar, salt, and fruit and vegetables. Secondary outcomes included changes in body mass index (BMI), blood pressure, haemoglobin, fasting plasma glucose (FPG), and serum lipids. Mixed-effects linear regression models were used to determine the net change in the outcomes in the intervention group relative to the comparison group. RESULTS:Participants' mean age was 53 years, a majority were women (76%), most were married (90%) and 51% had completed a college degree. All families had mobile phones, and more than 90% of these families had access to Internet through mobile phones. The intervention group had significant net mean changes of -12.5 g/day (p<0.001), -11.4 g/day (p<0.001), -0.5 g/day (p<0.001), and +71.6 g/day (p<0.001) in the intake of fat, sugar, salt, and fruit and vegetables, respectively. Similarly, significant net changes occurred for secondary outcomes: BMI -0.25 kg/m2, diastolic blood pressure -2.77 mm Hg, FPG -5.7 mg/dl, and triglycerides -24.2mg/dl. The intervention had no effect on haemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, or high-density lipoprotein cholesterol. CONCLUSION:The IT-enabled 'SMART Eating' intervention was found to be effective in reducing fat, sugar, and salt intake, and increasing fruit and vegetable consumption among urban adults from diverse socio-economic backgrounds. TRIAL REGISTRATION:Clinical Trial Registry of India CTRI/2016/11/007457.

Project description:BACKGROUND:Consumer health information technology can improve patient engagement in their health care and assist in navigating the complexities of health care delivery. However, the consumer health information technology offerings of health systems are often driven by provider rather than patient perspectives and inadequately address patient needs, thus limiting their adoption by patients. Consideration given to patients as stakeholders in the development of such technologies may improve adoption, efficacy, and consumer health information technology resource allocation. OBJECTIVE:The aims of this paper were to measure patient interest in different health system consumer health information technology apps and determine the influence of patient characteristics on consumer health information technology interest. METHODS:Patients seen at the Cleveland Clinic Neurological Institute were electronically surveyed on their interest in using different consumer health information technology apps. A self-efficacy scale, Patient Health Questionnaire-9 depression screen, and EuroQol 5 dimensions health-related quality of life scale were also completed by patients. Logistic regression was used to determine the influence of patient characteristics on interest in consumer health information technology in the categories of self-management, education, and communication. RESULTS:The majority of 3852 patient respondents had an interest in all technology categories assessed in the survey. The highest interest was in apps that allow patients to ask questions of providers (3476/3852, 90.24%) and to schedule appointments (3211/3839, 83.64%). Patient interest in consumer health information technology was significantly associated with greater depression symptoms, worse quality of life, greater health self-efficacy, and smartphone ownership (P<.001 for all listed). CONCLUSIONS:Patients should be viewed as active stakeholders in consumer health information technology development and their perspectives should consistently guide development efforts. Health systems should consider focusing on consumer health information technologies that assist patients in scheduling appointments and asking questions of providers. Patients with depression should also be considered for targeted consumer health information technology implementation. Health self-efficacy is a valid predictor of consumer health information technology interest and may play a role in the utilization of consumer health information technologies. Health systems, broadly, should put forth greater effort to understand the needs and interests of patients in the consumer health information technology development process. Consumer health information technology design and implementation may be improved by understanding which technologies patients want.

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SMART Markers: collecting patient-generated health data as a standardized property of health information technology.

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SMART Markers: collecting patient-generated health data as a standardized property of health information technology.

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