Revolutionizing Gerontology: Implementing Learning Systems for Enhanced Elderly Care
Introduction
The global population is aging, leading to an increased demand for specialized healthcare tailored to the unique needs of older adults. Addressing this demand requires a transformative approach to healthcare systems, focusing on continuous improvement and adaptation. The Learning Health System (LHS) model offers a promising framework for achieving this goal in the field of gerontology. This article explores the application of LHS principles in elderly care, highlighting successful implementations, challenges, and future directions.
The Learning Health System: A Cyclical Approach to Better Care
A Learning Health System (LHS) entails a cyclical process of data generation from daily practice, analysis of the data to evolve knowledge, and application of the knowledge to the practice. The model was originally meant to improve values in healthcare through the effective implementation of research findings using electronic health records. However, the concept of LHS can be expanded to other areas, such as to improve quality of life among older adults. In that sense, continuously recording older people’s daily life and combining that information with health-related data would generate knowledge about the factors determining older people’s everyday life activities and how they relate to their longitudinal health outcomes and long-term care needs.
Core Principles of a Learning Health System
The Learning Health System is characterized by systematic use of data resources to support quality improvement specific to central questions. Crucial to a learning health system are data from the bedside leveraged to assess and improve care. To develop the Age‐Friendly Learning Healthcare System knowledge from data at the bedside must be translated to practice change. It is necessary to translate evidence from implementation at the bedside to ongoing improvement in implementing age-friendly care and documenting and measuring care actions to continually improve the cycle.
Age-Friendly Health Systems: A Cornerstone of Geriatric Learning
The Age-Friendly Health System was proposed by the Institute for Healthcare Improvement (IHI) as a comprehensive whole-person approach to health care for older patients centered around the 4Ms of care-What Matters, Medication, Mentation, and Mobility. This approach is intended to transform healthcare systems and improve safety, efficiency, and continuity of care for older adults by addressing multiple needs in healthcare encounters across a spectrum of care environments. Standardizing the 4Ms approach has necessarily involved relatively broad guidance for care steps that can be deployed across multiple healthcare systems. It is important for researchers and quality organizations to continue to identify and work toward optimal measurement and standardization of what matters and all 4Ms.
The 4Ms of Age-Friendly Care
The 4Ms framework provides a structured approach to geriatric care:
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What Matters: Alignment of care with older adults' goals is intended to guide care across all the 4Ms. Information related to What Matters is least likely of all Ms data to be present or accessible in the EHR. There are likely missed opportunities to engage older adult patients about what matters most to them, and it is exceedingly complex to measure and relate what matters most to patient outcomes
Medication: Screening for high-risk medications.
Mentation: Involves regular screening for dementia and depression in outpatient care and regular delirium assessment in inpatient stays so that delirium can be addressed early if it occurs.
Mobility: Includes screening for fall risk in outpatient care, ensuring patients have the opportunity to get up out of bed regularly when hospitalized, and providing physical therapy consults.
EHR-Based Documentation Metrics for the 4Ms
Essential to driving the successful and sustained Age‐Friendly Healthcare System are standardized metrics that can be captured in the electronic health record (EHR) and used to assess implementation and measure care actions. Crucial to implementation and assessment of the spread of the Age‐Friendly Health System are standardized measures of 4Ms care and electronic health record‐based documentation metrics reflecting the degree of 4Ms care. Specific measures for each M and for the composite measures focused solely on structured EHR data rather than including text notes based on the greater capacity for future scalability.
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Implementation of Learning Systems in Gerontology: Case Studies
Several healthcare systems have successfully implemented LHS principles to improve geriatric care. Two notable examples are University of Utah Health (UUH) and University of California San Francisco (UCSF).
University of Utah Health (UUH)
University of Utah Health (UUH) includes a large academic medical center in the Intermountain West region of the United States. Age‐friendly care is implemented in inpatient and outpatient care environments. University of Utah Health (UUH) is an academic medical center that achieved “committed to care excellence” in age‐friendly care in 2021 and has a long‐standing culture of quality improvement central to a learning health system.
Adapting UCSF's EHR Metrics
In this study, standardized electronic health record (EHR) measures of Age‐Friendly Health System care developed at University of California San Francisco were adapted to the context of University of Utah Health, closing a critical gap in scaling metrics. The methodological process included consultations between our biomedical informatics research team and clinical informatics and geriatrician clinician teams and adaptations to the structure and approach of code.
Technical Adaptations
The technical team identified general adaptations necessary for implementation including adapting the code to be suitable for the relational database management system used at University of Utah Health (Oracle), which differed from the system used at UCSF (Microsoft SQL Server). For all the UCSF‐provided SQL code, Microsoft SQL Server‐specific SQL syntax was converted to universal SQL or to Oracle‐specific SQL syntax. When there was a foundational assumption in the UCSF code that did not hold locally, changes to the structure and approach used in the code were made. Aspects of the adaptation to the University of Utah ended up being quite challenging given the complex and intricate nature of the original UCSF SQL code and measures.
Clinical Expert Consultations
To complement our clinical research team consultations, we conducted eight semi‐structured interviews with clinical experts familiar with the EHR documentation related to the inpatient 4Ms. Clinical experts interviewed included specialists in mobility and rehabilitation assessment and intervention, an emergency department physician, a geriatrician leader in 4Ms implementation in inpatient care, and an RN expert in delivery of 4Ms care in the UUH outpatient clinics. Interviewees supported identification of which of the candidate measures were in regular use within inpatient care and outpatient care at UUH. Interviewees also provided screenshots of clinician‐facing views of the relevant EHR items (e.g., flowsheets) to ease identification of related features in the back‐end database.
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Patient-Level Validation
After applying the 4Ms code, our team conducted a review of preliminary results with geriatrician clinician experts. In addition, we conducted a patient‐level validation step. We identified 10 cases per metric (e.g., per each assessment and action measure within each M).
Results of the UUH Implementation
During the 3-year study period, over 16,489 unique patients aged 65 or older were admitted to UU Hospital (25,070 admissions) with a mean length of stay of 6.08 days. Of the 16 measures implemented at UCSF, we were able to replicate 14 with minor adaptations based on local context. For example, a validated mobility assessment screening tool is AMPAC 6‐clicks. The UCSF mobility EHR metric includes documentation of a mobility goal accompanying the AMPAC. A minor adaptation of this measure was performed to include screening with AMPAC once daily and eliminated the mobility goal because this goal documentation is not part of practice in UU inpatient care. Of note, parallel to the UCSF process, some measures were assessed at the encounter level, some at the day level (based on 151,778 days of hospitalization), and some at the nursing shift level (with nearly 296,000 nursing shifts as the denominator).
Key Findings
- For 25,070 encounters during the study (98.4%), a review of high-risk medications was conducted at least once during the encounter.
- For 18,538 of these encounters (74.0%) no high-risk medications were received during the encounter in an action measure of care related to Medication.
- However, for only 10,113 encounters (40.3%) was a high-risk medication never administered.
- The specific measure with the second lowest rate of implementation assessed by documentation at UUH was an action measure related to Mobility, “Patient was free from any immobilizing tethers for 24 h prior to discharge” which was true for only 55,182 days (33.0%).
- For composite measures for individual Ms, UUH ranged from 0.31 for What Matters indicating that 31% of encounters had What Matters assessed or acted on during their encounter to 0.74 for Medication indicating that all documentation metrics of Medication assessed in this study were met.
- For all 4Ms, 0.50 or 50% of patient encounters had all 4Ms administered during their encounter.
Comparison with UCSF Data
In comparisons between UUH data and UCSF data, some similarities and differences were noted. The UUH inpatients had a higher proportion (86.2% compared to 58.9%) of White patients. The proportion of patients identified as Asian differed markedly (1.6% for Asian patients at UU compared to 19.3% Asian patients at UCSF). Proportion of included patients who were female was relatively similar (48.4% compared to 49.4%). For individual Ms composite measures, UCSF and UU had similar results for some Ms, and less for other Ms. For the medication composite, the UU value was 0.74 whereas the UCSF value was 0.67. For mentation, the UU value was 0.37 compared to 0.68 for UCSF. Mobility was similar across health systems, 0.58 for UCSF versus 0.51 for UU.
University of California San Francisco (UCSF)
University of California San Francisco (UCSF) developed electronic health record (EHR) documentation metrics for inpatient assessment of the 4Ms (What Matters, Medication, Mentation, and Mobility) based on the Institute for Healthcare Improvement's recommended care practice for an Age-Friendly Healthcare System. The UCSF team shared 4Ms documentation metrics and Structured Query Language code used to assess 4Ms care at UCSF.
Lessons Learned from the Replication Process
We learned valuable lessons during the replication process. These lessons included (1) the importance of translating Microsoft SQL Server‐specific SQL to universal or Oracle SQL which required modifications related to date and time fields to create parallel encounter level, day level, or shift level documentation metrics, (2) the key step of integration with clinicians' documenting processes during care assessed by interview to streamline the adaptation to site‐specific issues throughout the code review and adaptation process, and (3) communication across the team including UCSF members to ensure that changes or adaptations were understood across the team at both sites. UCSF incorporated EHR access logs in the SQL code. The use of this type of log data can allow for sophisticated analyses on clinical users' interactions with the EHR. However, access logs have extremely high volumes and can take extensive computational resources and time to process. The use of these log queries often led to timeout errors for database queries during the adaptation process and required query parameterization and partitioning to overcome. The identification of the optimal partitioning approach and partition size took substantial effort.
Internet of Things (IoT) Platforms for Gerontology
Novel information technology, such as smartphone applications and wearable devices that can track independent individuals in space and time may enable the generation of such data. However, adaptation to new technology is deeply affected by age-specific factors, such as cognitive aging. Although technology can facilitate older people’s lives and they are increasingly incorporating it into their routines, the elderly typically experience more difficulties in learning to use and operate technology than younger adults.
Tracking Systems
Regarding the above-mentioned issues associated with the development of a LHS for a healthy ageing society, we aimed to launch an Internet of Things (IoT) platform to track the location of older people living independently. Given the possibility that older people would not accept technology, we adopted a beacon technology for tracking the residents. Beacons are small, wireless transmitters that use low-energy Bluetooth technology to send signals. The senior residents were only asked to carry a 16 g card-sized beacon as they went around with their normal routines without requiring any extra operation. In total, 30 beacon receivers were utilized throughout the CCRC to detect information from the surrounding beacons that broadcasted packets of data at regular intervals and forward the data to the server.
Case Study: A Continuing Care Retirement Community (CCRC) in Kyoto, Japan
This study took place in a CCRC built over 11 hectares on a hill in an isolated area of Kyoto, Japan. The community has a total of 361 units (335 apartment units and 26 assisted living units) and 51 nursing care units, offering 24-hour health care at an onsite clinic, security services with built-in door and room sensors, and social and recreational activities. The residents typically start their life in the CCRC in their seventies and spend 10-15 years until death.
Methodology
With this IoT system, exact times to enter and exit from a radius of 10 to 30 m from the 30 locations were accumulated in a chronological order for each resident. The locations of the receivers were carefully determined through a process of trial and error, so that the approximate distances the residents moved in and around the CCRC and the approximate time spent around the receivers for social interaction could be estimated. The Institutional Review Board (IRB) of Kyoto University approved the study (R1669). We obtained written informed consent from all participants to use their information. We analyzed data anonymously using research IDs (resident names were securely linked to a research ID when making the individual feedback sheets) to ensure confidentiality.
Recruitment and Engagement
Because carrying the beacon card was completely at the discretion of residents, we intentionally recruited participants by holding a large event in September 2018 and remained open so that new residents could also participate. For the recruitment purpose, important information was conceptualized as an illustration, since visual images communicate information better than text. The aim of the activity was showed in a circle linkage between “knowing” (one’s life), “changing” (one’s behavior to a healthier one), “feeling” (what is happening in one’s life), and “connecting” (with people), which we believed would be the learning component of LHS from the user’s point of view. Additionally, an overview of the project was illustrated, emphasizing ease of use, which is a known factor to facilitate technology adaptation among seniors.
Feedback and Adaptation
Providing feedback helps encourage adaptation by beneficiary group . The feedback form was designed to be as simple as possible to avoid the burden of excessive information for senior adults while containing the necessary information, leading to a one-page, double-sided sheet of paper. The front page displayed three kinds of information: (1) approximate distances over three months in km, (2) approximate time spent in social interaction and (3) visited locations. The locations were shown on the CCRC map with different flower signs. The flowers had three versions varying in size or growth and plotted on the basis of the tercile in the frequency of visiting the location.
Results
The program participation rate increased gradually over 16 months. In total, 111 residents aged 67 to 97 years (mean age = 81.1 years, SD = 7.4) participated. Among them, 70.4 % were females. Nearly 90% of participants were consistently carrying the beacon card during their everyday activities. Distances captured by the beacons indicated that participants’ average daily distance was slightly less than 1 km. Considerable reduction in the covered distance was observed during the summer (Quarter 3), but not with respect to the time spent on social interaction.
Potential Applications
To date, real-time behavior logs from over 100 functionally independent older people are being continuously generated using the beacon technology. These behavior logs could potentially be complemented with additional information, such as medical information and staff documentation, within the CCRC to formulate the “performance to data” component of the LHS, aiming at a healthy ageing society. Further, as the next step, we plan to situate a beacon-linked communication device in the CCRC with a careful consideration of age-specific characteristics for technology use.
Challenges and Limitations
The results of our study should be interpreted in the context of some limitations. The clinical implementation of 4Ms is well underway at the University of Utah, but not all areas of the hospital were participating during the study period. The study also included times of massive disruption of the healthcare system related to COVID‐19 which had a disproportionate impact on care for older adults. The approach to the 4Ms framework here was focused solely on structured EHR data. This is a crucial step for future assessments across healthcare systems and regions. However, it is important to note that there are profound challenges to capturing What Matters data using structured EHR metrics.
Capturing "What Matters"
What Matters includes understanding of a patient's core values. There are likely missed opportunities to engage older adult patients about what matters most to them, and it is exceedingly complex to measure and relate what matters most to patient outcomes.
Future Directions
Future research should focus on refining EHR metrics, incorporating patient-reported outcomes, and developing interventions to address gaps in care. It is also crucial to explore the use of advanced analytics and machine learning to identify patterns and predict outcomes in older adults.
Expanding the Scope of Data Collection
Designing a platform for ordinary older people to input data on their everyday life is, however, challenging. Barriers include issues with data privacy and technology adaptation. These behavior logs could potentially be complemented with additional information, such as medical information and staff documentation, within the CCRC to formulate the “performance to data” component of the LHS, aiming at a healthy ageing society. Further, as the next step, we plan to situate a beacon-linked communication device in the CCRC with a careful consideration of age-specific characteristics for technology use.
tags: #learning #system #in #gerontology
