Patient transfers are nonvalue-added activities. This study demonstrates that a detailed patient flow analysis with from-to charts can substantially shorten transfer distances, thereby minimizing extraneous patient and personnel movements. This reduction supports productivity improvement, cross-professional teamwork, and patient safety by placing all patient flow activities close to each other. Thus, this method is a valuable additional tool in hospital design.
Patient flow analysis brings patients to the core of hospital design by formulating target models for care unit’s patient flow and layout. Each patient transfer is a potential source of deviations as well as waste in the treatment process. In the target model the functions creating the main patient flow can be placed next to each other. In this study, a patient flow analysis was conducted in an orthopaedic trauma unit. IA target model was designed for patient flow in which the operational management of the patient flow is the responsibility of one person. To implement the target model, the unit’s operating theatres, ward, x-ray, and outpatient clinic should be placed as close to each other as possible, with x-ray and a plaster cast/orthoses room located in the interface between the ward and outpatient clinic. This target model eliminates around 5000 patient transfers between organisation units, reducing the number into half seen in the current situation. The target model also improves informal interaction between professional teams.
Objective: The aim of the study is to test a new nurse movement route analysis (NMRA) method for measuring nurses’ traffic volume between rooms on wards. Background: The World Health Organization calls for urgent investment in nurses. On the other hand, the challenges in the availability, direct care activity, and staffing of registered nurses make increasing the quality of care by process improvement a central objective for nursing. Method: The method is based on cellular operations with from/to matrix that describes nurse movements between rooms on a ward. The NMRA can be implemented by traditional manual observation or with a novel internet-of-things solution named SKAnalysis. Results: The greatest nurse flows led to patient rooms, nurses’ stations, and medicine rooms. The manual NMRA recorded a total of 3,040 room visits by nurses; visits to patient rooms accounted for 33% of all room visits, while visits to nurses’ stations accounted for 28%, and visits to the medicine room for 10%. The internet-of-things NMRA recorded a total of 25,841 room visits by nurses; patient room visits accounted for about 43% of all room visits, while nurses’ station visits accounted for 26% and medicine room visits for about 8%. Based on the results, researchers present the development examples and priorities for nursing. Conclusions: NMRA works and is a new universal method for analyzing nurses’ traffic which is a basic premise for improving working methods and productivity on the wards. Internet-of-things solution makes the implementation of NMRA six times more efficient than by the manual NMRA.
Objective: The objective of this study is present how a patient movement-based patient-flow analysis is performed for planning the new Heart Hospital of Tampere University Hospital and how patient transfer distances can be shortened by this method. Background: The Heart Hospital had served patients as a service line organization for years. However, the Heart Hospital layout rather looked like functional layout instead of service line layout because the units of the Heart Hospital have been spread out around the large university hospital campus. Method: The flow routes of patients treated over the course of 1 year were analyzed by information technology systems in the hospital planning phase. Then, the proximity ranking of the main functions of the Heart Hospital was made. Layout planning was performed based on the proximity ranking. Nine months after the opening of the new Heart Hospital, the distances between the various hospital functions were calculated for the old Heart Hospital and the new one. Results: In the old Heart Hospital, patients’ transfer distance was 5,654 km (3,513 miles), while the corresponding figure for the new Heart Hospital was 3,797 km (2,359 miles), which means the distance was reduced by 33%. Conclusion: The patient-flow analysis works as it generated substantially shorter patient transfer distances in the new Heart Hospital. Shorter distances have supported more fluent patient flows that, in turn, has contributed higher productivity and quality of care.
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