This systematic literature review provides information on the use of mixed methods research in human factors and ergonomics (HFE) research in health care. Using the PRISMA methodology, we searched four databases (PubMed, PsycInfo, Web of Science, and Engineering Village) for studies that met the following inclusion criteria: (1) field study in health care, (2) mixing of qualitative and quantitative data, (3) HFE issues, and (4) empirical evidence. Using an iterative and collaborative process supported by a structured data collection form, the six authors identified a total of 58 studies that primarily address HFE issues in health information technology (e.g., usability) and in the work of healthcare workers. About two-thirds of the mixed methods studies used the convergent parallel study design where quantitative and qualitative data were collected simultaneously. A variety of methods were used for collecting data, including interview, survey and observation. The most frequent combination involved interview for qualitative data and survey for quantitative data. The use of mixed methods in healthcare HFE research has increased over time. However, increasing attention should be paid to the formal literature on mixed methods research to enhance the depth and breadth of this research.
Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it.
Despite progress on patient safety since the Institute of Medicine 1999 report “To Err is Human”, significant problems remain. Human factors and systems engineering (HF/SE) has been increasingly recognized and advocated for its value in understanding, improving, and redesigning processes for safer care, especially for complex interacting sociotechnical systems. Broad awareness and adoption of HF/SE into safety improvement work, however, has been frustratingly slow. We provide an overview of HF/SE, its demonstrated value to a wide range of patient safety problems, in particular medication safety, and challenges to its broader implementation across health care. We propose seven recommendations and policy implications to maximize the spread of HF/SE, including formal and informal education programs, greater adoption of HF/SE by healthcare organizations, expanded funding to foster greater clinician-engineer partnerships, and coordinated national efforts to design and operationalize a system for spreading HF/SE into health care nationally.
The COVID-19 pandemic disrupted the world in 2020 by spreading at unprecedented rates and causing tens of thousands of fatalities within a few months. The number of deaths dramatically increased in regions where the number of patients in need of hospital care exceeded the availability of care. Many COVID-19 patients experience Acute Respiratory Distress Syndrome (ARDS), a condition that can be treated with mechanical ventilation. In response to the need for mechanical ventilators, designed and tested an emergency ventilator (EV) that can control a patient’s peak inspiratory pressure (PIP) and breathing rate, while keeping a positive end expiratory pressure (PEEP). This article describes the rapid design, prototyping, and testing of the EV. The development process was enabled by rapid design iterations using additive manufacturing (AM). In the initial design phase, iterations between design, AM, and testing enabled a working prototype within one week. The designs of the 16 different components of the ventilator were locked by additively manufacturing and testing a total of 283 parts having parametrically varied dimensions. In the second stage, AM was used to produce 75 functional prototypes to support engineering evaluation and animal testing. The devices were tested over more than two million cycles. We also developed an electronic monitoring system and with automatic alarm to provide for safe operation, along with training materials and user guides. The final designs are available online under a free license. The designs have been transferred to more than 70 organizations in 15 countries. This project demonstrates the potential for ultra-fast product design, engineering, and testing of medical devices needed for COVID-19 emergency response.
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