David T. Marc

Human factors issues in the intensive care unit.

In the context of an aging population, more critically ill patients, and a change in intensive care unit (ICU) services stemming from advances in technology, prevalent medical errors and staff burnout in the ICU are not surprising. The ICU provides ample opportunity for human factors experts to apply their knowledge about the strengths and weaknesses of human capabilities to design more effective care delivery. Human factors experts design work processes, technology, and environmental factors to effectively and constructively channel the attention and behavior of those providing care; a few areas of focus can have marked impacts on care delivery and patient outcomes. In this review, we focus on these 3 areas and investigate the solutions and problems addressed by previous research.

Why do physicians have difficulty accepting clinical decision support systems

Clinical decision support systems (CDSS) have not consistently demonstrated improvements in clinical care. This may partly be due to the lack of user acceptance. The purpose of this paper was to conduct a systematic literature review and task analysis to develop a model for CDSS design in order to achieve user acceptance.

Usability evaluation of laboratory information systems

Background: Numerous studies have revealed widespread clinician frustration with the usability of electronic health records (EHRs) that is counterproductive to adoption of EHR systems to meet the aims of health-care reform. With poor system usability comes increased risk of negative unintended consequences. Usability issues could lead to user error and workarounds that have the potential to compromise patient safety and negatively impact the quality of care.[1] While there is ample research on EHR usability, there is little information on the usability of laboratory information systems (LISs). Yet, LISs facilitate the timely provision of a great deal of the information needed by physicians to make patient care decisions.[2] Medical and technical advances in genomics that require processing of an increased volume of complex laboratory data further underscore the importance of developing user-friendly LISs. This study aims to add to the body of knowledge on LIS usability. Methods: A survey was distributed among LIS users at hospitals across the United States. The survey consisted of the ten-item System Usability Scale (SUS). In addition, participants were asked to rate the ease of performing 24 common tasks with a LIS. Finally, respondents provided comments on what they liked and disliked about using the LIS to provide diagnostic insight into LIS perceived usability. Results: The overall mean SUS score of 59.7 for the LIS evaluated is significantly lower than the benchmark of 68 (P < 0.001). All LISs evaluated received mean SUS scores below 68 except for Orchard Harvest (78.7). While the years of experience using the LIS was found to be a statistically significant influence on mean SUS scores, the combined effect of years of experience and LIS used did not account for the statistically significant difference in the mean SUS score between Orchard Harvest and each of the other LISs evaluated. Conclusions: The results of this study indicate that overall usability of LISs is poor. Usability lags that of systems evaluated across 446 usability surveys.

Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Term Coverage.

As part of the Open Government Initiative, the United States federal government published datasets to increase collaboration, transparency, consumer participation, and research, and are available online at HealthData.gov. Currently, HealthData.gov does not adequately support the accessibility goal of the Open Government Initiative due to issues of retrieving relevant data because of inadequately cataloguing and lack of indexing with a standardized terminology. Given the commonalities between the HealthData.gov and MEDLINE metadata, Medical Subject Headings (MeSH) may offer an indexing solution, but there needs to be a formal evaluation of the efficacy of MeSH for covering the dataset concepts. The purpose of this study was to determine if MeSH adequately covers the HealthData.gov concepts. The noun and noun phrases from the HealthData.gov metadata were extracted and mapped to MeSH using MetaMap. The frequency of no exact, partical and no matches with MeSH terms were determined. The results of this study revealed that about 70% of the HealthData.gov concepts partially or exactly matched MeSH terms. Therefore, MeSH may be a favorable terminology for indexing the HealthData.gov datasets.