How providers can best accommodate patient-reported data
New technologies have implementation challenges, including interoperability, patient privacy, and accuracy and reliability concerns.
The healthcare industry is moving toward consumer-centered, patient-driven care. The consumer and patient perspective is becoming central to how healthcare is delivered, reimbursed and evaluated by regulators and other stakeholders.
Previously, individual patients reported on their health to their individual clinicians, with little opportunity for the quantitative and qualitative data to be aggregated and mobilized at a population level. Alternatively, patients had to participate in clinical registries or studies to provide their data to the health system for research purposes only.
In recent years, however, many new patient- and consumer-directed tools and platforms have become available. The new tools and platforms—like the Apple Health Kit, Apple Watch and FitBit—have enabled patients to report on their health outcomes to their healthcare providers and insurers, and to gather their health data for their own personal use.
These health tracking applications have all made meaningful strides into the consumer health market. Some consumer health tracking tools have even made the leap to support clinical care by integrating with medical records or tracking clinically-relevant biomarkers, like the Apple Watch’s heart monitoring app, which can detect irregular heart rhythm up to 99 percent of the time. The National Institutes of Health has even entered the fray, launching the All of Us initiative in May 2018, seeking clinical and patient-reported data on 1 million Americans.
Clearly, patient-reported data can encompass a broad collection of data elements and data sources. From passive health trackers like FitBit to health reporting platforms like PatientsLikeMe, patient-reported outcomes data can be in registries or health records and other clinically validated self-reported health measures of pain or anxiety.
Some healthcare leaders have expressed concern about the poor quality of the data that can be collected through consumer products like the Apple Watch or FitBit. As many users of the tools will know, sometimes they don’t even count steps accurately, shedding new light on the possibility of the overdiagnosis and treatment of atrial fibrillation, for example.
To combat the fear of poor quality data, the National Institutes of Health Common Fund is pursuing initiatives like PROMIS that are focused on developing “a rigorously tested patient reported outcome measurement tool.” Particularly when healthcare registry leaders are considering incorporating patient-reported outcomes measures into their registries to support clinical decision-making, prioritizing high-quality, rigorous data collection practices is crucial.
While the new technologies, tools and platforms sound great in theory, there are important implementation challenges. As leaders in healthcare delivery, administration and management look to the future of patient-reported data and how it can be used to improve the quality of care and patient outcomes, leaders should consider both familiar challenges like interoperability and patient privacy, and new questions on the topic of scientific accuracy and reliability of the data.
Challenge 1: Interoperability
Patient-reported outcomes data is particularly relevant for interoperability initiatives because of the disparate sources from which the data may be derived. New consumer tools that collect patient data result in large volumes of data from various sources that are not standardized and may not meet interoperability standards, rendering it difficult or impossible to integrate with other data used in clinical decision-making.
Challenge 2: Patient privacy and security
As patient-reported data proliferates, patients, clinicians and other stakeholders are both aware of and concerned about data privacy and security. Patient privacy and security are particularly relevant because of the increased scrutiny on data privacy more broadly, following the full implementation of the General Data Protection Regulation (GDPR) in May 2018 and the California Consumer Privacy Act (CCPA), going into full effect in 2020.
Challenge 3: Data accuracy and reliability
Some clinicians and researchers have expressed concern that the data collected by wearable devices or through self-report health measures is inaccurate. Poor data collection methods or patients falsifying their data or outcomes may cause inaccuracies.
What actions can healthcare leaders take to address these challenges?
As for interoperability, government regulators are starting to create interoperability standards for healthcare data, including some for patient-reported outcomes in a clinical setting. However, rigorous interoperability standards for all types of patient reported data, such as the data collected from wearable devices are unlikely to come from regulators.
In 2018, the Agency for Healthcare Research and Quality announced its challenge “to develop health IT capable of enabling EHR integration of patient-reported outcomes data,” demonstrating the growing interest in high-quality patient-reported outcomes measures that can be used in clinical settings.
Whether they come from private organizations or the government, interoperability initiatives that minimize the risk of choosing the “wrong” platform will make healthcare leaders more amenable to gather and use patient-reported data. Healthcare IT leaders currently have too many platform options to collect patient data. Integrating data from multiple sources is currently quite expensive, especially when it requires use of proprietary tools and platforms. Initiatives that focus on data systems design, including technology architecture and guidance on how to select the best platform, will make the transition to multiple patient-reported data sources more straightforward.
In regards to patient data privacy and security, increased activity in privacy regulation, such as the CCPA, should reassure patients about sharing their data. However, any inappropriate use or perception of impropriety could make patients unwilling to share their data, rendering it useless for health improvement initiatives.
For now, wearables, for example, tend to be poorly regulated from a privacy perspective, but the full implementation of GDPR last May led many wearables companies to introduce new rigor to their privacy policies, making them more suitable for reporting health data appropriately.
Leaders in healthcare technology must take care to protect identifiable health data when it is used in an organizational context. Leaders should also implement and publicize privacy measures to entice and reassure patients that their data will be kept confidential and that the patients will reap the benefits of the use of their data.
As for data accuracy and reliability, studies have found that patient-reported data is closely correlated with validated clinical measures in most cases. In fact, some patient-reported measures have long been used in clinical settings because of the difficulty of capturing them in quantitative biomarkers. For example, the gold standard of pain assessment is self-reported measures.
Furthermore, data from wearable devices has been proven to be highly accurate: in addition to the example of the Apple Watch’s heart monitoring app, mainstream consumer health tracking devices provide reliable measures of heart rate, number of steps taken, distance traveled and sleep duration.
Additional fine-tuning and development will make wearables robust and clinically relevant tools for healthcare providers. Healthcare leaders can work with technology companies to validate the patient- or user-reported outcomes in both clinical and non-clinical settings.
Patients and consumers are becoming more interested and engaged in their care, but the patient voice has largely remained absent from their health record. Patient-reported data may fill that gap and provide clinicians, payers, regulators and industry partners with additional clarity on patients’ health, informing individuals’ treatment and population-level interventions.
As the healthcare system becomes more patient-centric, healthcare leaders should deliver and assess care in ways that matter to patients, seeking data from the patients themselves. Not only can patient-reported data improve an individual’s care and health outcomes, it can also empower patients. Patient-reported data will enable patients to learn from others in similar health situations and help them to make informed treatment choices based on their health priorities.
Previously, individual patients reported on their health to their individual clinicians, with little opportunity for the quantitative and qualitative data to be aggregated and mobilized at a population level. Alternatively, patients had to participate in clinical registries or studies to provide their data to the health system for research purposes only.
In recent years, however, many new patient- and consumer-directed tools and platforms have become available. The new tools and platforms—like the Apple Health Kit, Apple Watch and FitBit—have enabled patients to report on their health outcomes to their healthcare providers and insurers, and to gather their health data for their own personal use.These health tracking applications have all made meaningful strides into the consumer health market. Some consumer health tracking tools have even made the leap to support clinical care by integrating with medical records or tracking clinically-relevant biomarkers, like the Apple Watch’s heart monitoring app, which can detect irregular heart rhythm up to 99 percent of the time. The National Institutes of Health has even entered the fray, launching the All of Us initiative in May 2018, seeking clinical and patient-reported data on 1 million Americans.
Clearly, patient-reported data can encompass a broad collection of data elements and data sources. From passive health trackers like FitBit to health reporting platforms like PatientsLikeMe, patient-reported outcomes data can be in registries or health records and other clinically validated self-reported health measures of pain or anxiety.
Some healthcare leaders have expressed concern about the poor quality of the data that can be collected through consumer products like the Apple Watch or FitBit. As many users of the tools will know, sometimes they don’t even count steps accurately, shedding new light on the possibility of the overdiagnosis and treatment of atrial fibrillation, for example.
To combat the fear of poor quality data, the National Institutes of Health Common Fund is pursuing initiatives like PROMIS that are focused on developing “a rigorously tested patient reported outcome measurement tool.” Particularly when healthcare registry leaders are considering incorporating patient-reported outcomes measures into their registries to support clinical decision-making, prioritizing high-quality, rigorous data collection practices is crucial.
While the new technologies, tools and platforms sound great in theory, there are important implementation challenges. As leaders in healthcare delivery, administration and management look to the future of patient-reported data and how it can be used to improve the quality of care and patient outcomes, leaders should consider both familiar challenges like interoperability and patient privacy, and new questions on the topic of scientific accuracy and reliability of the data.
Challenge 1: Interoperability
Patient-reported outcomes data is particularly relevant for interoperability initiatives because of the disparate sources from which the data may be derived. New consumer tools that collect patient data result in large volumes of data from various sources that are not standardized and may not meet interoperability standards, rendering it difficult or impossible to integrate with other data used in clinical decision-making.
Challenge 2: Patient privacy and security
As patient-reported data proliferates, patients, clinicians and other stakeholders are both aware of and concerned about data privacy and security. Patient privacy and security are particularly relevant because of the increased scrutiny on data privacy more broadly, following the full implementation of the General Data Protection Regulation (GDPR) in May 2018 and the California Consumer Privacy Act (CCPA), going into full effect in 2020.
Challenge 3: Data accuracy and reliability
Some clinicians and researchers have expressed concern that the data collected by wearable devices or through self-report health measures is inaccurate. Poor data collection methods or patients falsifying their data or outcomes may cause inaccuracies.
What actions can healthcare leaders take to address these challenges?
As for interoperability, government regulators are starting to create interoperability standards for healthcare data, including some for patient-reported outcomes in a clinical setting. However, rigorous interoperability standards for all types of patient reported data, such as the data collected from wearable devices are unlikely to come from regulators.
In 2018, the Agency for Healthcare Research and Quality announced its challenge “to develop health IT capable of enabling EHR integration of patient-reported outcomes data,” demonstrating the growing interest in high-quality patient-reported outcomes measures that can be used in clinical settings.
Whether they come from private organizations or the government, interoperability initiatives that minimize the risk of choosing the “wrong” platform will make healthcare leaders more amenable to gather and use patient-reported data. Healthcare IT leaders currently have too many platform options to collect patient data. Integrating data from multiple sources is currently quite expensive, especially when it requires use of proprietary tools and platforms. Initiatives that focus on data systems design, including technology architecture and guidance on how to select the best platform, will make the transition to multiple patient-reported data sources more straightforward.
In regards to patient data privacy and security, increased activity in privacy regulation, such as the CCPA, should reassure patients about sharing their data. However, any inappropriate use or perception of impropriety could make patients unwilling to share their data, rendering it useless for health improvement initiatives.
For now, wearables, for example, tend to be poorly regulated from a privacy perspective, but the full implementation of GDPR last May led many wearables companies to introduce new rigor to their privacy policies, making them more suitable for reporting health data appropriately.
Leaders in healthcare technology must take care to protect identifiable health data when it is used in an organizational context. Leaders should also implement and publicize privacy measures to entice and reassure patients that their data will be kept confidential and that the patients will reap the benefits of the use of their data.
As for data accuracy and reliability, studies have found that patient-reported data is closely correlated with validated clinical measures in most cases. In fact, some patient-reported measures have long been used in clinical settings because of the difficulty of capturing them in quantitative biomarkers. For example, the gold standard of pain assessment is self-reported measures.
Furthermore, data from wearable devices has been proven to be highly accurate: in addition to the example of the Apple Watch’s heart monitoring app, mainstream consumer health tracking devices provide reliable measures of heart rate, number of steps taken, distance traveled and sleep duration.
Additional fine-tuning and development will make wearables robust and clinically relevant tools for healthcare providers. Healthcare leaders can work with technology companies to validate the patient- or user-reported outcomes in both clinical and non-clinical settings.
Patients and consumers are becoming more interested and engaged in their care, but the patient voice has largely remained absent from their health record. Patient-reported data may fill that gap and provide clinicians, payers, regulators and industry partners with additional clarity on patients’ health, informing individuals’ treatment and population-level interventions.
As the healthcare system becomes more patient-centric, healthcare leaders should deliver and assess care in ways that matter to patients, seeking data from the patients themselves. Not only can patient-reported data improve an individual’s care and health outcomes, it can also empower patients. Patient-reported data will enable patients to learn from others in similar health situations and help them to make informed treatment choices based on their health priorities.
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