Patient Generated Health Data
Patient generated health data (PGHD) is any data created, recorded or gathered by patients (or their designees) to help address a specific healthcare concern. PGHD can take any form from sophisticated reads from a device to a handwritten list of symptoms.
The rapid expansion of digital innovation, including mHealth apps and remote devices is empowering consumers to become active participants in their own care. This panel explores the implications of this trend.
Is wearable technology the future?
Wearable technology is one of the fastest growing areas of the tech industry. It enables users to monitor and track their physical health, such as heart rate, blood pressure, weight and sleep patterns. It is used to track progress of an exercise routine, to improve performance in sports or to help manage an illness like diabetes or asthma.
Patient generated health data (PGHD) is digital information that is actively captured, recorded and transmitted by patients (or their families or caregivers) through commercial tools such as smartphone apps, fitness or activity tracking devices and mHealth eHealth software. It is distinct from clinically collected PGHD collected in clinic settings or through encounters with providers because patients are primarily responsible for capturing and recording this data and determining how to share it.
This shift toward a consumer-led model of healthcare has the potential to improve access to care, reducing costs and barriers. It will also enable a greater diversity of data sources to augment EHRs, medical studies and clinical experience, constructing a more holistic view of patients’ health at both the micro and macro level.
Will wearables reduce incidence of disease?
While smartphone and wearable devices have the ability to collect a myriad of continuous multimodal data, there are currently barriers that prevent these devices from being used effectively. The need for standardization and easy-to-use tools are a must.
In addition to traditional activity monitors that capture daily step counts, the research community is uncovering digital health markers that go beyond these standard metrics. For example, a recent study found that higher daily steps were associated with lower risks of a diagnosis of obesity, sleep apnea, gastroesophageal reflux disease (GERD) and major depressive disorder.
However, Alissa emphasized that these metrics need to be integrated into healthcare systems and physician workflows to be useful. In order for physicians to make use of this information, it is crucial that the devices support interoperability standards that allow physicians to access and integrate this data into patient records or analytics. Additionally, the industry needs to address reimbursement models that can ensure sustainable implementation and adoption of this technology.
Will wearables reduce incidence of presentations to ED iin Australia?
A number of challenges remain to be overcome for wearables to become useful in clinical settings. On the hardware side, overcoming power constraints and the integration of multiple sensing modalities in small form factors remains challenging. Pantelopoulos et al. created a scoring system for the patient/wearer, manufacturer and supervising physician to evaluate wearable devices’ usability and found that the majority of systems fall short in this regard due to battery life constraints and on-body hardware size [165]. In addition, sensor data accuracy and interpretation remains to be improved. Leth et al. demonstrated that step count measures are only accurate at slow walking speeds and that wearable heart rate sensors may misinterpret low activity as resting HR, which can lead to over-exercising and decreased physical health outcomes.
The ability to monitor and present actionable health insights from device data could revolutionize ED triage by providing valuable information about the most urgent patients. However, implementing these devices in a clinical setting requires addressing the challenges of interpreting and integrating device data with existing clinical decision support (CDS) systems, creating new standards for collecting and transporting sensitive information from wearables to electronic health records, and developing closed loop intervention systems.
Will wearables improve patient outcomes?
As health systems design digital health interventions, they must consider clinician and patient needs. A common theme identified in the two programs reviewed by this workgroup was that for wearables to be effective, they must enable easy access for clinicians to clinically relevant data and be easily integrated into a routine clinical workflow. These requirements were elicited directly from a number of clinicians involved in the program design and delivery at Kaiser and Ochsner.
These features, along with a standardised approach to collection for patient-reported outcomes, are required for the digital phenotyping of patients. Such a phenotyping process will lead to new tools for disease detection, tracking progression and response to treatment, and predictive models supporting patient-specific precision care. The ubiquity of healthcare data standards, including HL7 Fast Healthcare Interoperability Resources, in both care delivery and research spaces is also enabling a new generation of interoperable use cases for the integration of clinic- and patient-generated data.
OnePhenix is the only IPAAS software that connects your wearable data to your healthcare professionals. www.Onephenix.com.au
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