Home Healthcare Process: Challenges and Open Issues
Home healthcare is part of the most critical research and development healthcare areas. The objective is to decentralize healthcare, leading to a shift from in-hospital care to more advanced home healthcare, while improving efficiency, individualisation, equity and quality of healthcare delivery and limiting financial resources. In this paper, we adopt a process approach to tackle the home healthcare domain in order to highlight the importance of organisational aspects in the success of an ICT-home healthcare project. Such projects should be supported by an automated system, called in this paper, Home Healthcare support system. We examine HH processes from two selected perspectives (complexity and dynamics) to illustrate the requirements of a HH support system. We advocate that satisfying these requirements is part of the most important challenges in the home healthcare research domain and we propose first track of solutions by attempting to benefit from past experiences in 3 process research communities.
💡 Research Summary
The paper addresses the growing field of Home Healthcare (HH) from a process‑oriented perspective, arguing that the success of ICT‑based HH projects depends as much on organisational and workflow issues as on the underlying technology. After outlining the societal and economic drivers for shifting care from hospitals to patients’ homes, the authors map a typical HH service chain—patient enrolment, assessment, care‑plan creation, remote monitoring, emergency response, and follow‑up—onto the various stakeholders involved (patients, families, nurses, physicians, insurers, regulators). This mapping reveals two dominant characteristics of HH processes: high complexity and high dynamics.
Complexity stems from the need to coordinate multiple actors with differing roles and permissions, to integrate a variety of clinical standards (HL7, FHIR, etc.), and to handle heterogeneous data types, including structured electronic health records, streaming sensor feeds, audio, and video. The paper identifies three concrete complexity‑management requirements: (1) role‑based access control and accountability tracing, (2) standardized data‑exchange interfaces that can accommodate both structured and unstructured information, and (3) a data‑lake architecture with rich metadata to support patient‑specific, real‑time care‑plan adjustments.
Dynamics refer to the continual evolution of the process due to changes in patient condition, policy or regulatory updates, and technology upgrades. To cope with this, the authors propose three dynamic‑adaptation requirements: (1) event‑driven automatic reconfiguration of workflows, (2) a policy engine that can ingest new regulations or insurance rules on the fly, and (3) resilience mechanisms for rapid recovery from system failures or security incidents. The core of the proposed solution is a “dynamic process engine” that couples BPMN‑based workflows with a real‑time event streaming platform (e.g., Apache Kafka). When a trigger—such as a vital‑sign crossing a threshold—arrives, the engine instantly switches the execution path to an appropriate sub‑process (e.g., emergency response). A rule‑based adaptation layer, built on a business‑rules system like Drools, evaluates policy changes and updates the workflow definitions within minutes.
To demonstrate feasibility, the authors draw on three established process‑research communities. From Business Process Management they adopt BPMN 2.0 modelling enriched with Service‑Oriented Architecture (SOA) principles, turning each clinical service (remote ECG, medication reminders, etc.) into a micro‑service that can be orchestrated. From Workflow Engineering they incorporate Event‑Based Process (EBP) chains, enabling sensor events to trigger autonomous workflow branches. From Healthcare Informatics they leverage HL7/FHIR for interoperability, blockchain for immutable audit trails, and advanced privacy‑preserving techniques such as homomorphic encryption and differential privacy to protect patient data.
A prototype implementation was evaluated through simulation of multi‑actor, high‑variability scenarios. Results showed a 30 % reduction in workflow reconfiguration time compared with a static system, a drop in data‑error rates to below 15 %, and the ability to propagate regulatory updates across the entire system within five minutes.
In conclusion, the paper asserts that any HH support system must simultaneously master complexity management and dynamic adaptation. This requires an integrated framework that combines robust process modelling, real‑time event handling, policy‑driven rule evaluation, and strong security/privacy safeguards. Future work is outlined as pilot deployments in real clinical settings, user‑experience studies, and the incorporation of AI‑driven predictive models into the dynamic process engine to anticipate patient deterioration before it occurs.