How to implement information technology in the operating room and the intensive care unit

Factors Influencing Integration and Usability of Model-Informed Precision Dosing Software in the Intensive Care Unit

BACKGROUND

 Antimicrobial dosing in critically ill patients is challenging and model-informed precision dosing (MIPD) software may be used to optimize dosing in these patients. However, few intensive care units (ICU) currently adopt MIPD software use.

OBJECTIVES

 To determine the usability of MIPD software perceived by ICU clinicians and identify implementation barriers and enablers of software in the ICU.

METHODS

 Clinicians (pharmacists and medical staff) who participated in a wider multicenter study using MIPD software were invited to participate in this mixed-method study. Participants scored the industry validated Post-study System Usability Questionnaire (PSSUQ, assessing software usability) and Technology Acceptance Model 2 (TAM2, assessing factors impacting software acceptance) survey. Semistructured interviews were used to explore survey responses. The framework approach was used to identify factors influencing software usability and integration into the ICU from the survey and interview data.

RESULTS

 Seven of the eight eligible clinicians agreed to participate in the study. The PSSUQ usability scores ranked poorer than the reference norms (2.95 vs. 2.62). The TAM2 survey favorably ranked acceptance in all domains, except image. Qualitatively, key enablers to workflow integration included clear and accessible data entry, visual representation of recommendations, involvement of specialist clinicians, and local governance of software use. Barriers included rigid data entry systems and nonconformity of recommendations to local practices.

CONCLUSION

 Participants scored the MIPD software below the threshold that implies good usability. Factors such as availability of software support by specialist clinicians was important to participants while rigid data entry was found to be a deterrent.

Staff perceptions of the implementation of a trauma video review program at a level I trauma center

OBJECTIVES

Successful implementation of any new technology requires extensive engagement with front-line staff. We explored the perceptions of emergency department and trauma staff about a trauma video review program (TVR) prior to implementation of the first such program in Canada at our level I trauma center.

METHODS

We conducted semi-structured individual interviews and in situ small group interviews with 35 multidisciplinary ED and trauma staff members of a teaching and research hospital in Toronto, Canada. We sought maximum variation in the sample of purposively selected participants. Interviews were recorded with audiotapes or detailed field notes, transcribed verbatim, coded, and analyzed using standard thematic analysis techniques.

RESULTS

Participants expressed overall support for the concept of TVR, but there is a core sense of unease that influenced overall staff perceptions. Despite several departmental presentations, very few participants actually had a solid understanding of how the TVR worked. Many were apprehensive about their own professional privacy, deeply concerned about vulnerable patients being filmed without consent, and questioned how video data would be used. Despite significant hesitancy, ED and trauma staff identified positive opportunities that TVR could bring, including providing an evidence base for quality improvement.

CONCLUSIONS

TVR is an evolving approach to evaluate quality and patient safety in the trauma bay. As such it brings with it natural concerns and apprehension from staff regarding privacy, confidentiality, and how data will be captured and used. There is opportunity for these types of concerns to be addressed with a robust knowledge translation plan and engagement of staff throughout the implementation process.

Implementation of the Operating Room Black Box Research Program at the Ottawa Hospital Through Patient, Clinical, and Organizational Engagement: Case Study

Background

A large proportion of surgical patient harm is preventable; yet, our ability to systematically learn from these incidents and improve clinical practice remains limited. The Operating Room Black Box was developed to address the need for comprehensive assessments of clinical performance in the operating room. It captures synchronized audio, video, patient, and environmental clinical data in real time, which are subsequently analyzed by a combination of expert raters and software-based algorithms. Despite its significant potential to facilitate research and practice improvement, there are many potential implementation challenges at the institutional, clinician, and patient level. This paper summarizes our approach to implementation of the Operating Room Black Box at a large academic Canadian center.

Objective

We aimed to contribute to the development of evidence-based best practices for implementing innovative technology in the operating room for direct observation of the clinical performance by using the case of the Operating Room Black Box. Specifically, we outline the systematic approach to the Operating Room Black Box implementation undertaken at our center.

Methods

Our implementation approach included seeking support from hospital leadership; building frontline support and a team of champions among patients, nurses, anesthesiologists, and surgeons; accounting for stakeholder perceptions using theory-informed qualitative interviews; engaging patients; and documenting the implementation process, including barriers and facilitators, using the consolidated framework for implementation research.

Results

During the 12-month implementation period, we conducted 23 stakeholder engagement activities with over 200 participants. We recruited 10 clinician champions representing nursing, anesthesia, and surgery. We formally interviewed 15 patients and 17 perioperative clinicians and identified key themes to include in an information campaign run as part of the implementation process. Two patient partners were engaged and advised on communications as well as grant and protocol development. Many anticipated and unanticipated challenges were encountered at all levels. Implementation was ultimately successful, with the Operating Room Black Box installed in August 2018, and data collection beginning shortly thereafter.

Conclusions

This paper represents the first step toward evidence-guided implementation of technologies for direct observation of performance for research and quality improvement in surgery. With technology increasingly being used in health care settings, the health care community should aim to optimize implementation processes in the best interest of health care professionals and patients.

Implementing Medical Technological Equipment in the OR: Factors for Successful Implementations

Operating rooms (ORs) more and more evolve into high-tech environments with increasing pressure on finances, logistics, and a not be neglected impact on patient safety. Safe and cost-effective implementation of technological equipment in ORs is notoriously difficult to manage, specifically as generic implementation activities omit as hospitals have implemented local policies for implementations of technological equipment. The purpose of this study is to identify success factors for effective implementations of new technologies and technological equipment in ORs, based on a systematic literature review. We accessed ten databases and reviewed included articles. The search resulted in 1592 titles for review, and finally 37 articles were included in this review. We distinguish influencing factors and resulting factors based on the outcomes of this research. Six main categories of influencing factors on successful implementations of medical equipment in ORs were identified: “processes and activities,” “staff,” “communication,” “project management,” “technology,” and “training.” We identified a seventh category “performance” referring to resulting factors during implementations. We argue that aligning the identified influencing factors during implementation impacts the success, adaptation, and safe use of new technological equipment in the OR and thus the outcome of an implementation. The identified categories in literature are considered to be a baseline, to identify factors as elements of a generic holistic implementation model or protocol for new technological equipment in ORs.

Intensive care information system impacts

Introduction:

Today, intensive care needs to be increased with a prospect of an aging population and socioeconomic factors influencing health intervention, but there are some problems in the intensive care environments, it is essential to resolve. The intensive Care information system has the potential to solve many of ICU problems. The objective of the review was to establish the impact of intensive care information systems on the practitioners practice, patient outcomes and ICU performance.

Methods:

Scientific databases and electronic journal citations was searched to identify articles that discussed the impacts of intensive care information system on the practices, patient outcomes and ICU performance. A total of 22 articles discussing ICIS outcomes was included in this study from 609 articles initially obtained from the searches.

Results:

Pooling data across studies, we found that the median impact of ICIS on information management was 48.7%. The median impact of ICIS on user’ outcomes was 36.4%, impact on saving tips by 24%, clinical decision support by a mean of 22.7%, clinical outcomes improved by a mean of 18.6%, and researches improved by 18%.

Conclusion:

The functionalities of ICIS are growing day by day and new functionalities are available with every major release. Better adoption of ICIS by the intensive care environments emphasizes the opportunity of better intensive care services through patient oriented intensive care clinical information systems. There is an immense need for developing guidelines for standardizing ICIS to to maximize the power of ICISs and to integrate with HISs. This will enable intensivists to use the systems in a more meaningful way for better patient care. This study provides a better understanding and greater insight into the effectiveness of ICIS in improving patient care and reducing health care expenses.

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Background

The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique.

Methods

Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE).

Results

Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models.

Conclusions

A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.

Status quo and current trends of operating room management in Germany

PURPOSE OF REVIEW

Ongoing healthcare reforms in Germany have required strenuous efforts to adapt hospital and operating room organizations to the needs of patients, new technological developments, and social and economic demands. This review addresses the major developments in German operating room management research and current practice.

RECENT FINDINGS

The introduction of the diagnosis-related group system in 2003 has changed the incentive structure of German hospitals to redesign their operating room units. The role of operating room managers has been gradually changing in hospitals in response to the change in the reimbursement system. Operating room managers are today specifically qualified and increasingly externally hired staff. They are more and more empowered with authority to plan and control operating rooms as profit centers. For measuring performance, common perioperative performance indicators are still scarcely implemented in German hospitals. In 2008, a concerted time glossary was established to enable consistent monitoring of operating room performance with generally accepted process indicators. These key performance indicators are a consistent way to make a procedure or case - and also the effectiveness of the operating room management - more transparent.

SUMMARY

In the presence of increasing financial pressure, a hospital's executives need to empower an independent operating room management function to achieve the hospital's economic goals. Operating room managers need to adopt evidence-based methods also from other scientific fields, for example management science and information technology, to further sustain operating room performance.