Watson A. Bowes

An oil-based model of inhalation anesthetic uptake and elimination

An oil-based model was developed as a physical simulation of inhalation anesthetic uptake and elimination. It provides an alternative to animal models in testing the performance of anesthesia equipment. A 7.5-1 water-filled manometer simulates pulmonary mechanics. Nitrogen and carbon dioxide flowing into the manometer simulate oxygen consumption and carbon dioxide production. Oil-filled chambers (180 ml and 900 ml) simulate the uptake and washout of halothane by the vessel-rich and muscle tissue groups. A 17.2-1 air-filled chamber simulates uptake by the lung group. Gas circulates through the chambers (3.7, 13.8, and 25 l/min) to simulate the transport of anesthetic to the tissues by the circulatory system. Results show that during induction and washout, the rate of rise in endtidal halothane fraction simulated by the model parallels that measured in patients. The models end-tidal fraction changes correctly with changes in cardiac output and alveolar ventilation. The model has been used to test anesthetic controllers and to evaluate gas sensors, and should be useful in teaching principles underlying volatile anesthetic uptake.

Health information technology adoption: Understanding research protocols and outcome measurements for IT interventions in health care.

OBJECTIVE To classify and characterize the variables commonly used to measure the impact of Information Technology (IT) adoption in health care, as well as settings and IT interventions tested, and to guide future research. MATERIALS AND METHODS We conducted a descriptive study screening a sample of 236 studies from a previous systematic review to identify outcome measures used and the availability of data to calculate these measures. We also developed a taxonomy of commonly used measures and explored setting characteristics and IT interventions. RESULTS Clinical decision support is the most common intervention tested, primarily in non-hospital-based clinics and large academic hospitals. We identified 15 taxa representing the 79 most commonly used measures. Quality of care was the most common category of these measurements with 62 instances, followed by productivity (11 instances) and patient safety (6 instances). Measures used varied according to type of setting, IT intervention and targeted population. DISCUSSION This study provides an inventory and a taxonomy of commonly used measures that will help researchers select measures in future studies as well as identify gaps in their measurement approaches. The classification of the other protocol components such as settings and interventions will also help researchers identify underexplored areas of research on the impact of IT interventions in health care. CONCLUSION A more robust and standardized measurement system and more detailed descriptions of interventions and settings are necessary to enable comparison between studies and a better understanding of the impact of IT adoption in health care settings.

Development and classification of a robust inventory of near real-time outcome measurements for assessing information technology interventions in health care

OBJECTIVE To develop and classify an inventory of near real-time outcome measures for assessing information technology (IT) interventions in health care and assess their relevance as perceived by experts in the field. MATERIALS AND METHODS To verify the robustness and coverage of a previously published inventory of measures and taxonomy, we conducted semi-structured interviews with clinical and administrative leaders from a large care delivery system to collect suggestions of outcome measures that can be calculated with data available in electronic format for near real-time monitoring of EHR implementations. We combined these measures with the most commonly reported in the literature. We then conducted two online surveys with subject-matter experts to collect their perceptions of the relevance of the measures, and identify other potentially relevant measures. RESULTS With input from experienced health care leaders and informaticists, we developed an inventory of 102 outcome measures. These measures were classified into a taxonomy of commonly used measures around the categories of quality, productivity, and safety. Safety measures were rated as most relevant by subject-matter experts, especially those measuring medication processes. Clinician satisfaction and measures assessing mean time to complete tasks and time spent on electronic documentation were also rated as highly relevant. DISCUSSION By expanding the coverage of our previously published inventory and taxonomy, we expect to help providers, health IT vendors and researchers to more effectively and consistently monitor the impact of EHR implementations in near real-time, and report more standardized outcomes in future studies. We identified several measures not commonly assessed by previous studies of IT implementations, especially those of safety and productivity, which deserve more attention from the broader informatics community. CONCLUSION Our inventory of measures and taxonomy will help researchers identify gaps in their measurement approaches and report more standardized measurements of IT interventions that could be shared among researchers, hopefully facilitating comparison across future studies and increasing our understanding of the impact of IT interventions in health care.

Comprehensive methodology to monitor longitudinal change patterns during EHR implementations: a case study at a large health care delivery network

OBJECTIVE To test a systematic methodology to monitor longitudinal change patterns on quality, productivity, and safety outcomes during a large-scale commercial Electronic Health Record (EHR) implementation. MATERIALS AND METHODS Our method combines an interrupted time-series design with control sites and 41 consensus outcomes including quality (11 measures), productivity (20 measures), and safety (10 measures). The intervention consisted of a phased commercial EHR implementation at a large health care delivery network. Four medium-size hospitals and 39 clinics from 5 geographic regions implementing the new EHR were compared against a parallel control consisting of one medium-size and one large hospital and 10 clinics that had not implemented the new EHR at the time of this study. We collected monthly data from February 2013 to July 2017. RESULTS The proposed methodology was successfully implemented and significant changes were observed in most measured variables. A significant change attributable to the intervention was observed in 12 (29%) measures in three or more regions; in 32 (78%) measures in two or more regions; and in 40 (98%) measures in at least one region. A similar pattern (i.e., same impact in three or more regions) was detected for nine (22%) measures, a mixed pattern (i.e., same impact in two regions, and different impact in other regions) was detected for nine (22%) measures, and an inconsistent pattern (i.e., did not detect the same impact across regions) was detected for 23 (56%) measures. DISCUSSION Using a formal methodology to assess changes in a set of consensus measures, we detected various patterns of impact and mixed time-sensitive effects. With an increasing adoption of EHR systems, it is critical for health care organizations to systematically monitor their EHR implementations. The proposed method provides a robust and consistent approach to monitor EHR implementations longitudinally allowing for continuous monitoring after the system becomes stable in order to avoid unexpected effects. CONCLUSION Our results and methodology can guide the broader medical and informatics communities by informing what and how to continuously monitor EHR impact on quality, productivity, and safety.

A mechanical model to simulate uptake and elimination of inhalation anesthetics

Using olive oil to simulate body tissues, a mechanical model of uptake and elimination of inhalation anesthetics was developed. Body tissues were grouped according to anesthetic solubility and blood perfusion, and modeled with equivalent oil volumes. Changes in cardiac output were simulated by varying gas flows through the oil chambers. Alveolar ventilation was augmented by adjusting the minute ventilation setting of the ventilator. During 64 minutes of uptake and washout, the mechanical model end-tidal halothane concentration remained within 0.07 vol.% of a computer simulation. The model may prove useful in developing and testing anesthesia equipment without the need for animal experiments or human volunteers.<<ETX>>