Inquiry into Patient Work Systems in Home and Community Settings

HFE studies aimed at understanding patient work in home and community settings have, not surprisingly, typically focused on single chronic conditions such as heart failure (Mickelson et al., 2016), diabetes (Crowley et al., 2014), hypertension (Marquard et al., 2013), breast cancer (Gorman et al., 2018), chronic pain (Nio Ong et al., 2011), children with medical complexities (Valdez et al., 2020), and those with and at risk for HIV (Marquard et al., 2018).

For example, Zayas-Caban (2012) used home interviews and observations to identify myriad HIM tasks, public and private locations where these tasks occurred, and traditional and nontraditional health information storage artifacts. These variations showed the significant need for consumer health information technology designers to balance foundational intervention design elements that support all users, with tailored aspects of the intervention that support individual variations.

Mickelson et al. (2015) analyzed photographs, observation notes, interviews, video recordings, medical record data, and surveys to determine what cognitive artifacts older adults used to manage their medications, typically in their home environment. These artifacts, ranging from scales and blood pressure cuffs to electronic health record (EHR) medication lists, were largely viewed as necessary and helpful for medication management work. However, there was often conflicting or missing information across and within the cognitive artifacts older adults used to manage their medications. In addition, some artifacts were not appropriately designed to accommodate the needs of older users and the range in users’ access to appropriate technologies. The authors concluded that, to be most useful, paper- and technology- based artifacts must account for contexts of use in addition to users’ needs, limitations, abilities, tasks, and routines.

Mickelson et al. (2016) used observations and interviews framed by the SEIPS 2.0 framework, along with five well-known macrocognitive processes (sensemaking, planning, monitoring, decision-making, and coordinating), to describe the medication management work conducted by older adults with heart failure (Crandall et al., 2006; Patterson & Hoffman, 2012). Their descriptive research identified recommendations for technologies that would support these individuals’ macrocognitive patient work.

Look and Stone (2018) analyzed the complex work of medication management outside of formal healthcare settings from the perspective of older adults’ informal caregivers. Through focus groups with these caregivers, they identified two broad categories of medication management support: direct activities requiring physical handling of medications (e.g. sorting) and indirect cognitive activities (e.g. decisionmaking). Their analysis identified the crucial role of informal caregivers in medication management for older adults and the types of tools and strategies they use in their homes and communities to do their work.

As a result of collecting and analyzing data related to patient work in the home and community settings being time-intensive, it is important that HFE researchers consider how to make their data and analyses available to others. For example, using the previously described models and frameworks to guide data collection and analysis can aid in synthesizing quantitative and qualitative data across multiple studies. These data could ideally be used by myriad types of individuals— hardware and software designers, clinicians, and lay people—without burdening participants.

Redesigning and Evaluating Patient Work Systems in Home and Community Settings

The patient work panels made clear that much of the HFE work to date has been focused on describing patient work in home and community settings, rather than on patient work system design and/or evaluation. This is not surprising, as research in emerging fields often focuses (rightly) on understanding current work systems before attempting to alter them.

Redesign and evaluation of patient work systems in home and community settings are fraught with challenges. Formative design and evaluation (e.g. needs assessments, iterative usability testing)—aimed at improving the design of the intervention—are more common in home and community settings than summative evaluations, which assess the intervention outcomes such as those detailed in the SEIPS 2.0 structure- process-outcomes framework (Holden et al., 2013). Such a summative evaluation based on the SEIPS 2.0 framework would at a minimum consider how structural work system elements impact patient, professional, and collaborative patient/profes- sional processes, which then impact a range of outcomes. For formative design and summative evaluation, it is nearly impossible to include an appropriate representation of individuals, organizations, and internal environments in the evaluation, thereby reducing the external validity or generalizability of the findings. In the formative design and evaluation, this means a potentially narrow sample of participants are providing (still invaluable) guidance on intervention design choices. In the summative evaluation, the effectiveness for all will likely be judged based on the effectiveness for some. Although some studies may do well at recruiting a diverse range of participants based on their individual characteristics (e.g. age, ethnicity) (Valdez et ah, 2012), it is rare that they recruit from a diverse range of organizations (e.g. family roles and responsibilities) or internal environments (e.g. noise levels). It is even rarer that researchers attend to the impact of the intersectionality of these elements. If we believe these patient work system factors related to home and community settings have an impact on work processes and outcomes, as detailed in the SEIPS 2.0 and NRC. frameworks, they likely should be included in the identification of participants to include in the design and evaluation. However, because of the resource burdens associated with conducting field research in home and community settings, it is infeasible for researchers to include participants varying across all these intersecting characteristics. Rather, researchers must identify which characteristics they presume will most influence design choices and diversify their sample of participants based on those characteristics.

There is also significant tension in the choices of outcomes measured in evaluations of patient work system interventions and how those outcomes are measured. In general, HFE researchers and practitioners often focus on outcomes from a safety- performance-satisfaction triad, with different domains weighing these three types of outcomes differently (Lee et al., 2017). Interventions targeted at high-risk domains may prioritize safety, interventions targeted at lower-risk workplaces may prioritize performance, and consumer product developers may prioritize satisfaction (Lee et al., 2017). For patient work system interventions in home and community settings, it is not always clear how these factors should be prioritized.

Satisfaction is often measured by HFE researchers via measures of perceived usability, usefulness, or adoption and is typically viewed as a prerequisite required for the individual to initiate and continue engaging with the intervention (e.g. an mHealth device) (Ware et al., 2019). Researchers have found that aspects of home and community settings impact satisfaction. For example, Fischer et al. (2014) reviewed studies addressing elderly individuals’ use and acceptance of information technology for health. They identified that challenges with technologies were not only technical but based on the nature of the patient’s broader work system.

Safety is often viewed as an (unmeasured) requirement of the intervention, not as an ultimate outcome measure. More recently, two panels have focused on the role of patients in patient safety (Papautsky et al., 2018, 2019). Two of the 2018 panelists described contexts in home and community where patients play a role in improving patient safety, one involving improving medication safety practices and the other addressing how mobile apps and wearables may prevent visits to clinical settings (a location where significant safety issues occur). Studies measuring the impact of patient work interventions in home and community settings on safety will likely increase over time.

Measuring any of these outcomes for patient work interventions in home and community settings is significantly challenged by shifting organizational and internal environment factors over various time frames (hour-to-hour, month-to-month, year- to-year). For example, an mHealth device may be used throughout diverse locations (e.g. home, work, school) within a single day, and housing situations (e.g. moving from an apartment in a city to a home in a rural setting) along with family dynamics (e.g. birth or death of a family member) may shift over time. For the same individual, changes in one’s organizational and internal environment may improve or diminish patient work system redesign outcomes. The individual may also be changing other health-related behaviors (e.g. dietary or exercise routines, sleep habits, meditation) in parallel with the HFE redesigns. In addition, usual care (the control group) is also often changing over time. These challenges make teasing out the true impact of patient work system redesigns in home and community settings particularly challenging. Not surprisingly, many studies have found less than ideal outcomes for patient work system redesigns deployed in home and community settings (Beatty & Lambert, 2013; Yu et al., 2012), though this is not uncommon for interventions deployed in naturalistic settings.

In 1962, Rogers initially proposed the “Diffusion of Innovations theory” to explain how, why, and at what rate new ideas and technology spread (Rogers, 1962, 2004). Rogers divided individuals into five categories: innovators; early adopters; early majority; late majority; and laggards. There are now thousands of articles across many disciplines based on the Diffusion of Innovations theory. Researcher- driven technology-based patient work interventions in home and community settings are often deployed only after they are shown to be effective through lengthy evaluations, meaning technologies may be outdated by the time of broad dissemination. The technology platforms on which they are developed may therefore only be desirable to late adopters or laggards. If these technologies are deployed in home and community settings to individuals who were early adopters of those technologies, but have moved on to new technologies, the intervention may be deemed neither useful nor usable (e.g. web-based interventions without well-designed mobile viewing capacity). Commercially driven technology-based patient work interventions in home and community settings may be more suitable to early adopters, but may also have limited outcomes evaluation. This trade-off signals the need for HFE researchers and practitioners to determine where on the safety-performance-satisfaction outcomes triad a particular patient work intervention is located (Lee et al., 2017).

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