Adaptations of electronic health records to activate physicians’ knowledge: how can patient centered care be improved through technology?

Smart transition pathways and development incentive mechanism of China's smart community elderly care industry under market dominance: Considering a multi-subjective behavior game

Against the backdrop of an aging population, China is actively experimenting with an innovative elderly care model, so smart community elderly care has recently received widespread attention. However, the results of the implementation of the model have not yet met the expectation due to the variety of interests among the relevant participants. In this study, we identified the most core stakeholders in smart community elderly care, developed a four-party evolutionary game model including local governments, communities, service supply enterprises and households with elderly members. By applying the system dynamics method, we simulate the evolutionary paths and explore the complex interactions at the multiparticipant level in order to facilitate the transition of community elderly care services from traditional to smart, and then propose managerial insights for accelerating the construction of smart community elderly care. The results suggest that: (1) the four players in the game influence each other and are intimately related, and the benign interaction between them will further stimulate the vitality of the smart community elderly care industry; (2) appropriate improvement in policy support will strongly promote smart community elderly care, and the incentive effect on the demand side (households with elderly members) is more significant; (3) when households' preference for smart services increases, and the perceived value to communities and enterprises reaches a certain threshold, communities and enterprises will actively adopt smart solution and technology as well as develop stable portfolio strategy; (4) measures such as simultaneously increasing the level of smart and resource synergy will promote the system evolution toward smart services, and the system is more sensitive to the internal behavior of the enterprise than the external behavior between community and enterprise.

Evolutionary game analysis of community elderly care service regulation in the context of "Internet +"

Background

As an upgraded form of the elderly care service industry, "Internet + Community Elderly Care" integrates information technology, artificial intelligence, Internet thinking, and the construction of community elderly care service mechanisms. Research on "Internet + Community Elderly Care" has become a focus.

Methods

The four-party evolutionary game model of elderly service regulations was presented, which consists of the government, providers, platforms, and elderly people. By using Lyapunov stability theory, the stability of each player's strategy selection was analyzed. Furthermore, the sensitivity analysis of the key parameters was discussed in detail using system dynamics.

Results and discussion

Online evaluations of elderly people have more positive effects on the regulatory system than offline evaluations. Both the penalties on providers and subsidies on platforms given by the government have thresholds. Moreover, government penalties for providers and subsidies for platforms could curb their speculative behavior and enable effective steering of providers and platforms.

Conclusion

The Omni-feedback mechanism for elderly people can effectively curb the speculative behavior of elderly care service providers and elderly care service information platforms. The government should dynamically adjust penalties and subsidy policies.

Understanding complex work using an extension of the resilience CARE model: an ethnographic study

Background

Resilient Healthcare research centres on understanding and improving quality and safety in healthcare. The Concepts for Applying Resilience Engineering (CARE) model highlights the relationships between demand, capacity, work-as-done, work-as-imagined, and outcomes, all of which are central aspects of Resilient Healthcare theory. However, detailed descriptions of the nature of misalignments and the mechanisms used to adapt to them are still unknown.

Objective

The objectives were to identify and classify types of misalignments between demand and capacity and types of adaptations that were made in response to misalignments.

Methods

The study involved 88.5 hours of non-participant ethnographic observations in a large, teaching hospital in central London. The wards included in the study were: two surgical wards, an older adult ward, a critical care unit, and the Acute Assessment Unit (AAU), an extension unit created to expedite patient flow out of the Emergency Department. Data were collected via observations of routine clinical work and ethnographic interviews with healthcare professionals during the observations. Field notes were transcribed and thematically analysed using a combined deductive-inductive approach based on the CARE model.

Results

A total of 365 instances of demand-capacity misalignment were identified across the five wards included in the study. Of these, 212 had at least one observed corresponding work adaptation. Misalignments identified include equipment, staffing, process, communication, workflow, and space. Adaptations identified include process, resource redistribution, and extra-role performance. For all misalignment types observed across the five in-patient settings, process adaptations were the most frequently used adaptations. The exception to this was for staffing misalignments, which were most frequently responded to with extra-role performance adaptations. Of the three process adaptations, hospital workers most often adapted by changing how the process was done.

Conclusions

This study contributes a new version of the CARE model that includes types of misalignments and corresponding adaptations, which can be used to better understand work-as-done. This affords insight into the complexity of the system and how it might be improved by reducing misalignments via work system redesign or by enhancing adaptive capacity.