ABSTRACT
In the mid-1980s, the anesthesia departments at hospitals affiliated with Harvard Medical School were faced with a challenge: mounting medical malpractice costs. Malpractice insurance was provided by the Controlled Risk Insurance Company (CRICO), a patient safety and medical malpractice insurance company owned by and providing service to the Harvard medical community. CRICO spearheaded an effort to reduce these costs and ultimately found a way to decrease the risks associated with anesthesia. Here, we chronicle events that led to the dramatic changes in medical practice that resulted from the activities of a small group of concerned anesthesiologists at Harvard-affiliated hospitals. We place these events in a historical perspective and explore how other specialties followed this example, and end with current strategies that minimize the risk associated with anesthesia. We conducted interviews with principals who formulated original standards of patient monitoring. In addition, we consulted documents in the public domain and primary source material. Efforts of these pioneers resulted in the establishment of the seminal Harvard-based anesthesia monitoring standards for minimal monitoring. What followed was an unprecedented transformation of the entire field. After the implementation of these standards at Harvard-affiliated hospitals, the American Society of Anesthesiologists (ASA) adopted "Standards for Basic Anesthetic Monitoring" for use during the administration of all anesthetics in the United States. Other nations have since adopted similar guidelines and these practices have resulted in significant improvements in patient safety. Currently, we estimate mortality due to anesthesia in healthy patients to be 1:400,000-perhaps as much as 10 times lower since the early 1980s. What began as an attempt to lower medical malpractice costs in a group of university hospitals became a worldwide effort that resulted in improvements in patient safety. Other specialties have adopted similar measures. Currently, an attitude and appreciation of safety are exemplified by several practices that include among others-the adherence to these patient safety guidelines, simulator training, the promulgation of standards and guidelines by ASA, and the use of a safety checklist before induction of anesthesia.
Subject(s)
Anesthesia Department, Hospital/standards , Anesthesia/standards , Anesthesiologists/standards , Monitoring, Intraoperative/standards , Practice Patterns, Physicians'/standards , Anesthesia/adverse effects , Anesthesia/history , Anesthesia Department, Hospital/history , Anesthesiologists/history , Boston , Guideline Adherence/standards , History, 20th Century , History, 21st Century , Humans , Insurance, Liability , Malpractice , Monitoring, Intraoperative/history , Patient Safety/standards , Practice Guidelines as Topic/standards , Practice Patterns, Physicians'/history , Quality Improvement/standards , Quality Indicators, Health Care/standards , Risk Assessment , Risk FactorsABSTRACT
BACKGROUND: In 2016 the German Society of Anesthesiology and Intensive Care Medicine (DGAI) and the Association of German Anesthetists (BDA) published 10 quality indicators (QI) to compare and improve the quality of anesthesia care in Germany. So far, there is no evidence for the feasibility of implementation of these QI in hospitals. OBJECTIVE: This study tested the hypothesis that the implementation of the 10 QI is feasible in German hospitals. MATERIAL AND METHODS: This prospective three-phase national multicenter quality improvement study was conducted in 15 German hospitals and 1 outpatient anesthesia center from March 2017 to February 2018. The trial consisted of an initial evaluation of pre-existing structures and processes by the heads of the participating anesthesia departments, followed by a 6-month implementation phase of the QI as well as a final re-evaluation phase. The implementation procedure was supported by web-based implementation aids ( www.qi-an.org ) and internal quality management programs. The primary endpoint was the difference in the number of implemented QI per center before and after implementation. Secondary endpoints were the number of newly implemented QI per center, the overall number of successful implementations of each QI, the identification of problems during the implementation as well as the kind of impediments preventing the QI implementation. RESULTS: The average number of implemented QI increased from 5.8 to 6.8 (mean of the differences 1.1⯱ 1.3; Pâ¯< 0.01). Most frequently the QI perioperative morbidity and mortality report (5 centers) and the QI temperature management (4 centers) could be implemented. After the implementation phase, the QI incidence management and patient blood management were implemented in all 16 centers. Implementation of other quality indicators failed mainly due to a lack of time and lack of structural resources. CONCLUSION: In this study the implementation of QI was proven to be mostly feasible in the participating German hospitals. Although several QI could be implemented with minor effort, more time, financial and structural resources would be required for some QI, such as the QI postoperative visit.
Subject(s)
Anesthesia/standards , Quality Improvement/standards , Anesthesia Department, Hospital/standards , Germany , Hospitals , Humans , Prospective Studies , Quality Assurance, Health CareABSTRACT
BACKGROUND: Benchmarking group surgical anesthesia productivity continues to be an important but challenging goal for anesthesiology groups. Benchmarking is important because it provides objective data to evaluate staffing needs and costs, identify potential operating room management decisions that could reduce costs or improve efficiency, and support ongoing negotiations and discussions with health system leadership. Unfortunately, good and meaningful benchmarking data are not readily available. Therefore, a survey of academic anesthesiology departments was done to provide current benchmarking data. METHODS: A survey of members of the Society of Academic Associations of Anesthesiology and Perioperative Medicine (SAAAPM) was performed. The survey collected data by facility and included type of facility, number and type of staff and anesthetizing sites each weekday, and the billed American Society of Anesthesiologists (ASA) units and number of cases over 12 months. The facility types included academic medical center (AMC), community hospital (Community), children's hospital (Children), and ambulatory surgical center (ASC). All anesthesia care billed using ASA units were included, except for obstetric anesthesia. Any care not billed or billed using relative value units (RVUs) were excluded. Percentage of nonoperating room anesthetizing sites, staffing ratio, and surgical anesthesia productivity measurements "per case" and "per site" were calculated. RESULTS: Of the 135 society members, 63 submitted complete surveys for 140 facilities (69 AMC, 26 Community, 7 Children, and 38 ASC). In the survey, overall median productivity for AMC and Children was similar (12,592 and 12,364 total ASA units per anesthetizing site), while the ASC had the lowest median overall productivity (8911 total ASA units per anesthetizing site). By size of facility, in the survey, the smaller facilities (<10 sites, ASC or non-ASC) had lower median overall productivity as compared to larger facilities. For AMC and Children, >20% of anesthetizing sites were nonoperating room anesthetizing sites. Anesthesiology residents worked primarily in AMC and Children. In ASC and Community, residents worked only in 18% and 35% of facilities, respectively. More than half the AMCs reported at least 1 break certified nurse anesthetist (CRNA) each day. CONCLUSIONS: To make data-driven decisions on clinical productivity, anesthesiology leaders need to be able to make meaningful comparisons at the facility level. For a group that provides care in multiple facilities, one can make internal comparisons among facilities and follow measurements over time. It is valuable for leaders to also be compare their facilities with industry-wide measurements, in other words, benchmark their facilities. These results provide benchmarking data for academic anesthesiology departments.
Subject(s)
Academic Medical Centers/standards , Anesthesia Department, Hospital/standards , Benchmarking/standards , Efficiency , Personnel Staffing and Scheduling/standards , Quality Indicators, Health Care/standards , Workload/standards , Health Care Surveys , Hospital Bed Capacity/standards , Hospitals, High-Volume/standards , Hospitals, Low-Volume/standards , Humans , Operating Rooms/standardsABSTRACT
BACKGROUND: We implemented a new policy at our institution where the responsibility for intensive care unit (ICU) patient transports to the operating room (OR) was changed from the anesthesia to the ICU service. We hypothesized that this approach would be associated with increased on-time starts and decreased turnover times. METHODS: In the historical model, intubated patients or those on mechanical circulatory assistance (MCA) were transported by the anesthesia service to the OR ("pre-ICU Pickup"). In our new model, these patients are transported by the ICU service to the preoperative holding area (Pre-op) where care is transferred to the anesthesia service ("post-ICU Transfer"). If judged necessary by the ICU or anesthesia attending, the patient was transported by the anesthesia service ("post-ICU Pickup"). We retrospectively reviewed case tracking data for patients undergoing surgery before (January 2014 to May 2015) and after implementation (July 2016 to June 2017) of the new policy. The primary outcome was the proportion of elective, weekday first-case, on-time starts. To adjust for confounders including comorbidities and time trends, we performed a segmented logistic regression analysis assessing the effect of our intervention on the primary outcome. Secondary outcomes were turnover times and compliance with preoperative checklist documentation. RESULTS: We identified 95 first-start and 86 turnover cases in the pre-ICU Pickup, 70 first-start and 88 turnover cases in the post-ICU Transfer, and 6 turnover cases in the post-ICU Pickup group. Ignoring time trends, the crude proportion of on-time starts increased from 32.6% in the pre-ICU Pickup to 77.1% in the post-ICU Transfer group. After segmented logistic regression adjusting for age, sex, American Society of Anesthesiologists (ASA) physical status, Sequential Organ Failure Assessment (SOFA) score, respiratory failure, endotracheal intubation, MCA, congestive heart failure (CHF), valvular heart disease, and cardiogenic and hemorrhagic shock, the post-ICU Transfer group was more likely to have an on-time start at the start of the intervention than the pre-ICU Pickup group at the end of the preintervention period (odds ratio, 11.1; 95% confidence interval [CI], 1.3-125.7; P = .043). After segmented linear regression adjusting for the above confounders, the estimated difference in mean turnover times between the post-ICU Pickup and pre-ICU Transfer group was not significant (-6.9 minutes; 95% CI, -17.09 to 3.27; P = .17). In post-ICU Transfer patients, consent, history and physical examination (H&P), and site marking were verified before leaving the ICU in 92.9%, 93.2%, and 89.2% of the cases, respectively. No adverse events were reported during the study period. CONCLUSIONS: A transition from the anesthesia to the ICU service for transporting ICU patients to the OR did not change turnover times but resulted in more on-time starts and high compliance with preoperative checklist documentation.
Subject(s)
Anesthesia Department, Hospital/standards , Critical Illness/therapy , Intensive Care Units/standards , Transportation of Patients/standards , Workflow , Adult , Aged , Anesthesia Department, Hospital/methods , Female , Humans , Male , Middle Aged , Retrospective Studies , Transportation of Patients/methodsABSTRACT
BACKGROUND: Digitization is spreading exponentially in medical care, with improved availability of electronic devices. Guidelines and standard operating procedures (SOPs) form an important part of daily clinical routine, and adherence is associated with improved outcomes. OBJECTIVE: This study aimed to evaluate a digital solution for the maintenance and distribution of SOPs and guidelines in 2 different anesthesiology departments in Switzerland. METHODS: A content management system (CMS), WordPress, was set up in 2 tertiary-level hospitals within 1 year: the Department of Anesthesiology and Pain Medicine at the Kantonsspital Lucerne in Lucerne, Switzerland, as an open-access system, followed by a similar system for internal usage in the Department of Anaesthesiology and Pain Medicine of the Inselspital, Bern University Hospital, in Bern, Switzerland. We analyzed the requirements and implementation processes needed to successfully set up these systems, and we evaluated the systems' impact by analyzing content and usage. RESULTS: The systems' generated exportable metadata, such as traffic and content. Analysis of the exported metadata showed that the Lucerne website had 269 pages managed by 44 users, with 88,124 visits per month (worldwide access possible), and the Bern website had 341 pages managed by 35 users, with 1765 visits per month (access only possible from within the institution). Creation of an open-access system resulted in third-party interest in the published guidelines and SOPs. The implementation process can be performed over the course of 1 year and setup and maintenance costs are low. CONCLUSIONS: A CMS, such as WordPress, is a suitable solution for distributing and managing guidelines and SOPs. Content is easily accessible and is accessed frequently. Metadata from the system allow live monitoring of usage and suggest that the system be accepted and appreciated by the users. In the future, Web-based solutions could be an important tool to handle guidelines and SOPs, but further studies are needed to assess the effect of these systems.
Subject(s)
Anesthesia Department, Hospital/standards , Information Dissemination , Internet , Practice Guidelines as Topic , Humans , SwitzerlandABSTRACT
BACKGROUND: We implemented a previously described barcode-based drug safety system in all of our anesthetizing locations. Providers were instructed to scan the barcode on syringes using our Anesthesia Information Management System before drug administration, but the rate of provider adherence was low. We studied an implementation intervention intended to increase the rate of scanning. METHODS: Using our Anesthesia Information Management System and Smart Anesthesia Manager software, we quantified syringe drug administrations by anesthesia providers with and without barcode scanning. We use an anesthesia team model in which an attending anesthesiologist is paired with a certified registered nurse anesthetist (CRNA) or a resident. Our system identified the pair of providers associated with a particular drug administration, but did not distinguish which providers actually administered the drug. Therefore, the rate of barcode scanning for a particular case was assigned to both providers equally. A baseline rate of scanning was established over a period of 17 months. An audit and feedback intervention was then performed that consisted of monthly performance reports sent by email to individual providers along with coffee gift card awards for top performers. The coffee gift cards were awarded in only the first 2 months of the intervention, while the email performance reports continued on a monthly basis. The coffee card awards were made public. The monthly emails reported the individual provider's rank order of performance relative to other providers, but was otherwise anonymous. The baseline rate of scanning was compared to the rate of scanning after the intervention for a period of 7 months. RESULTS: From November 2014 to March 2017, we accumulated 60,197 cases performed by 88 attending anesthesiologists, 65 CRNAs, and 148 residents. The total number of syringe drug administrations was 653,355. Average scanning performance improved from 8.7% of syringe barcodes scanned during the baseline period from November 2014 to February 2016 to 64.4% scanned during the period September 2016 to March 2017 (P < .001). Variation in performance among individuals was marked, ranging from 0% to 100% of syringes scanned. The performance of some individuals showed marked oscillation over time. There was greater variation in performance attributable to residents than in performance attributable to CRNAs. CONCLUSIONS: Feedback of individual provider performance data from the anesthesia information system to providers can be used in conjunction with other measures to improve performance. Despite improved average performance, there was marked variation in performance between individuals, and some individuals had marked oscillation of their performance over time.
Subject(s)
Anesthesiologists/standards , Anesthetics/administration & dosage , Drug Labeling/standards , Formative Feedback , Guideline Adherence/standards , Medication Systems, Hospital/standards , Nurse Anesthetists/standards , Practice Patterns, Nurses'/standards , Practice Patterns, Physicians'/standards , Reward , Anesthesia Department, Hospital/standards , Anesthesiologists/education , Anesthesiologists/psychology , Anesthetics/adverse effects , Attitude of Health Personnel , Health Knowledge, Attitudes, Practice , Humans , Internship and Residency , Medical Audit , Nurse Anesthetists/psychology , Prospective Studies , Quality Improvement/standards , Quality Indicators, Health Care/standardsABSTRACT
INTRODUCTION: The UNE 179003:2013 standard requires compliance with protocols to reduce the risks of patients from adverse events. METHODS: A description is presented of the procedure used in the Hospital Povisa to achieve UNE 179003:2010 certification for the intensive care unit, surgical division, and post-anaesthesia recovery unit (PARU). This was based on a risk management system, focusing on pro-active analysis using failure modes and effects analysis (FMEA) with the description of causes, consequences, risk weighting, and specific risk-minimising measures. A description is also presented of the analysis of reported adverse events (reactive analysis) in the Safety in Anesthesia and Resuscitation (SENSAR) notification system and the measures implemented over an eight-year period. RESULTS: The UNE 179003:2010 certification was obtained in July 2012, and the re-certification was achieved in July 2015. A total of 66 potential risks were established, which were weighted using a risk probability index (RPI), and measures were implemented that reduced this RPI by half. It also reflects the analysis of 1114 events declared in the SENSAR system over the past eight years, allowing for the introduction of 2681 measures, of which 98.4% are fully implemented. CONCLUSION: The application of the risk management methodology allowed (a) to improve safety in the area of action by reducing the risk to which the patients are subject, and (b) to gain certification in the UNE 179003 standard.
Subject(s)
Anesthesia Department, Hospital/standards , Certification , Critical Care/standards , Intensive Care Units/standards , Patient Safety/standards , Quality Improvement , Risk Management/standards , Surgery Department, Hospital/standards , Humans , Spain , Time FactorsABSTRACT
BACKGROUND: Anesthesia information management systems make prior anesthesia records readily available for review when patients return for a subsequent procedure but may create a problem of too much documentation to review in a limited amount of time. We implemented a screening tool to facilitate the identification of critical documentation for review. METHODS: An algorithm was developed to electronically search prior anesthesia records for predefined critical events and flag records containing these events. Our web-based daily case schedule was modified to contain a warning message for any patient on the schedule who has a prior record flagged by the system, in addition to a preexisting hyperlink to view the relevant record. A retrospective analysis was performed to determine the impact of the warning messages on the frequency with which the care team reviewed these records before providing anesthesia care. RESULTS: The screening algorithm flagged 13% of archived cases as critical. There were 3329 and 3369 cases in the 6 months before and after system implementation, respectively, that had prior critical records available for review at that time. One or more of these critical records were viewed before the subsequent case start in 39% vs 59% (P < .01) of cases in the pre- versus postimplementation periods. Subgroup analysis revealed that the increase was greatest for attending anesthesiologists working alone. CONCLUSIONS: We created a system to automatically detect critical events in prior anesthesia records for the purpose of forewarning the anesthesia care team when the same patient returns for another procedure. Inclusion of these warnings on the daily case schedule was associated with an increased frequency of preanesthesia review of old records.
Subject(s)
Anesthesia Department, Hospital/methods , Medical Records Systems, Computerized , Preoperative Care/methods , Anesthesia Department, Hospital/standards , Humans , Medical Records Systems, Computerized/standards , Preoperative Care/standardsABSTRACT
OBJECTIVE: To evaluate the quality of the services provided by the anaesthesia department of the San Cecilio Clinical University Hospital, from the health professionals' point of view. MATERIAL AND METHODS: Location: Andalusia. PARTICIPANTS: 134 health professionals in contact with the hospital anaesthesia department. Tool: self-administered questionnaire, measuring: accessibility, personal treatment, comfort, scientific and technical quality (scale 1 to 5), overall satisfaction (scale 0 to 10), and suggestions for improvement. A descriptive statistical and correlation analysis were performed, including mean differences (by sex, frequency of contact with the anaesthesia department, and unit), as well as a regression model. RESULTS: The quality of personal treatment received a mean of 4.2 points (SD 0.651), the scientific and technical quality 4.00 points (SD 0.532), accessibility 3.3 (SD 0.795), professional comfort 3.30 (SD 0.988), and patient comfort 2.62 points (SD 1.051). Overall satisfaction obtained a mean of 7.1 points (0 to 10 scale). Women and professionals working in units with less than 10 people had a better general evaluation of the anaesthesia department. The worse perspective was that of staff with daily contact with the anaesthesia department. Among the suggestions for improvement there were: Reducing waiting lists, creating special rooms to give information to families, improving working conditions, training and work satisfaction for staff, and achieving better communication and collaboration between health professionals. CONCLUSIONS: The internal evaluation shows the need for training strategies and organisational interventions in the anaesthesia department, in order to achieve a better quality and satisfaction for both professionals and patients.
Subject(s)
Anesthesia Department, Hospital/standards , Attitude of Health Personnel , Quality of Health Care , Cross-Sectional Studies , Female , Hospitals, University , Humans , MaleABSTRACT
Six Sigma and Lean methodologies are effective quality improvement tools in many health care settings. We applied the DMAIC methodology (define, measure, analyze, improve, control) to address deficiencies in our pediatric anesthesia supply chain. We defined supply chain problems by mapping existing processes and soliciting comments from those involved. We used daily distance walked by anesthesia technicians and number of callouts for missing supplies as measurements that we analyzed before and after implementing improvements (anesthesia cart redesign). We showed improvement in the metrics after those interventions were implemented, and those improvements were sustained and thus controlled 1 year after implementation.
Subject(s)
Anesthesia Department, Hospital/standards , Anesthesia/standards , Hospitals, Pediatric/standards , Quality Improvement/standards , Quality of Health Care/standards , Total Quality Management/standards , Anesthesia/methods , Anesthesia/trends , Anesthesia Department, Hospital/methods , Anesthesia Department, Hospital/trends , Follow-Up Studies , Hospitals, Pediatric/trends , Humans , Quality Improvement/trends , Quality of Health Care/trends , Total Quality Management/methods , Total Quality Management/trendsABSTRACT
BACKGROUND: Several countries have launched public reporting systems based on quality indicators (QIs) to increase transparency and improve quality in health care organizations (HCOs). However, a prerequisite to quality improvement is successful local QI implementation. The aim of this study was to explore the pathway through which a mandatory QI of the French national public reporting system, namely the quality of the anesthesia file (QAF), was put into practice. METHOD: Seven ethnographic case studies in French HCOs combining in situ observations and 37 semi-structured interviews. RESULTS: A significant proportion of potential QAF users, such as anesthetists or other health professionals were often unaware of quality data. They were, however, involved in improvement actions to meet the QAF criteria. In fact, three intertwined factors influenced QAF appropriation by anesthesia teams and impacted practice. The first factor was the action of clinical managers (chief anesthetists and head of department) who helped translate public policy into local practice largely by providing legitimacy by highlighting the scientific evidence underlying QAF, achieving consensus among team members, and pointing out the value of QAF as a means of work recognition. The two other factors related to the socio-material context, namely the coherence of information systems and the quality of interpersonal ties within the department. CONCLUSIONS: Public policy tends to focus on the metrological validity of QIs and on ranking methods and overlooks QI implementation. However, effective QI implementation depends on local managerial activity that is often invisible, in interaction with socio-material factors. When developing national quality improvement programs, health authorities might do well to specifically target these clinical managers who act as invaluable mediators. Their key role should be acknowledged and they ought to be provided with adequate resources.
Subject(s)
Hospitals/standards , Quality Improvement/standards , Quality Indicators, Health Care/organization & administration , Anesthesia Department, Hospital/standards , France , Group Practice/standards , Humans , Patient Care Team/standards , Qualitative ResearchABSTRACT
OBJECTIVE: The overall aim of this study was to describe the current set of guidelines for the preparation and care for children with autism spectrum disorder (ASD) in the perioperative setting across Sweden and explore the content of these guidelines in detail. METHOD: An online questionnaire was distributed to the chairpersons of all anesthesia departments (n = 68) and pediatric departments (n = 38) throughout Sweden. Follow-up phone calls were made to those departments that did not return the questionnaire. The presence of guidelines was analyzed through descriptive statistics. These guidelines and comments on routines used in these departments were analyzed inspired by conventional content analysis. RESULTS: Seven of the 68 anesthesia departments and none of the 38 pediatric departments across Sweden have guidelines for preparing and/or administering care to children with ASD within the perioperative setting. From the guidelines and routines used, 3 categories emerge: "lacking the necessary conditions," "no extra considerations needed," and "care with specific consideration for children with ASD." These 3 categories span a continuum in the care. In the first category, the anesthesia induction could result in the child with ASD being physically restrained. In the last category, the entire encounter with the health care service would be adapted to the specific needs of the child. CONCLUSION: There is a lack of evidence-based guidelines specifically designed to meet the needs of children with ASD in the preoperative period in Sweden. Further research is needed to understand if children with ASD would benefit from evidence-based guidelines.
Subject(s)
Anesthesia Department, Hospital/standards , Autism Spectrum Disorder , Hospitals, Pediatric/standards , Practice Guidelines as Topic/standards , Preoperative Care/standards , Child , Health Care Surveys , Humans , SwedenABSTRACT
BACKGROUND: Communication errors cause clinical incidents and adverse events in relation to surgery. To ensure proper postoperative patient care, it is essential that personnel remember and recall information given during the handover from the operating theatre to the postanaesthesia care unit. Formalizing the handover may improve communication and aid memory, but research in this area is lacking. OBJECTIVE: The objective of this study was to evaluate whether implementing the communication tool Situation-Background-Assessment-Recommendation (SBAR) affects receivers' information retention after postoperative handover. DESIGN: A prospective intervention study with an intervention group and comparison nonintervention group, with assessments before and after the intervention. SETTING: The postanaesthesia care units of two hospitals in Sweden during 2011 and 2012. PARTICIPANTS: Staff involved in the handover between the operating theatre and the postanaesthesia care units within each hospital. INTERVENTION: Implementation of the communication tool SBAR in one hospital. MAIN OUTCOME MEASURES: The main outcome was the percentage of recalled information sequences among receivers after the handover. Data were collected using both audio-recordings and observations recorded on a study-specific protocol form. RESULTS: Preintervention, 73 handovers were observed (intervention group, nâ=â40; comparison group, nâ=â33) involving 72 personnel (intervention group, nâ=â40; comparison group, nâ=â32). Postintervention, 91 handovers were observed (intervention group, nâ=â44; comparison group, nâ=â47) involving 57 personnel (intervention group, nâ=â31; comparison group, nâ=â26). In the intervention group, the percentage of recalled information sequences by the receivers increased from 43.4% preintervention to 52.6% postintervention (Pâ=â0.004) and the SBAR structure improved significantly (Pâ=â0.028). In the comparison group, the corresponding figures were 51.3 and 52.6% (Pâ=â0.725) with no difference in SBAR structure. When a linear regression generalised estimating equation model was used to account for confounding influences, we were unable to show a significant difference in the information recalled between the intervention group and the nonintervention group over time. CONCLUSION: Compared with the comparison group with no intervention, when SBAR was implemented in an anaesthetic clinic, we were unable to show any improvement in recalled information among receivers following postoperative handover. TRIAL REGISTRATION: Current controlled trials http://www.controlled-trials.com Identifier: ISRCTN37251313.
Subject(s)
Anesthesia Department, Hospital/standards , Continuity of Patient Care/standards , Early Medical Intervention/standards , Patient Care Team/standards , Patient Handoff/standards , Postoperative Care/standards , Anesthesia Department, Hospital/methods , Early Medical Intervention/methods , Female , Humans , Male , Postoperative Care/methods , Prospective Studies , Recovery Room/standardsABSTRACT
BACKGROUND: The use and organisation of medical technology has an important role to play for patient and user safety in anaesthesia. OBJECTIVES: Specification of the recommendations of the German Coalition for Patient Safety (APS) for users and operators of anaesthesia equipment, explore opportunities and challenges for the safe use and organisation of anaesthesia devices. METHODS: We conducted a literature search in Medline/PubMed for studies dealing with the APS recommendations for the prevention of medical device-related risks in the context of anaesthesia. In addition, we performed an internet search for reports and recommendations focusing on the use and organisation of medical devices in anaesthesia. Identified studies were grouped and assigned to the recommendations. The division into users and operators was maintained. RESULTS: Instruction and training in anaesthesia machines is sometimes of minor importance. Failure to perform functional testing seems to be a common cause of critical incidents in anaesthesia. There is a potential for reporting to the federal authority. Starting points for the safe operation of anaesthetic devices can be identified, in particular, at the interface of staff, organisation, and (anaesthesia) technology. CONCLUSIONS: The APS recommendations provide valuable information on promoting the safe use of medical devices and organisation in anaesthesia. The focus will be on risks relating to the application as well as on principles and materials for the safe operation of anaesthesia equipment.
Subject(s)
Anesthesia Department, Hospital/organization & administration , Anesthesia Department, Hospital/standards , Equipment Safety/standards , Equipment and Supplies, Hospital/adverse effects , Equipment and Supplies, Hospital/standards , Equipment and Supplies/adverse effects , Equipment and Supplies/standards , Health Care Coalitions , Medical Errors/prevention & control , Patient Safety , Germany , Humans , Quality Improvement/organization & administration , Quality Improvement/standardsABSTRACT
BACKGROUND: The American Society of Anesthesiologists has embraced the concept of the Perioperative Surgical Home as a means through which anesthesiologists can add value to the health systems in which they practice. One key listed element of the Perioperative Surgical Home is to support "scheduling initiatives to reduce cancellations and increase efficiency." In this study, we explored the potential benefits of the Perioperative Surgical Home with respect to inpatient cancellations and add-on case scheduling. We evaluated 6 hypotheses related to the timing of inpatient cancellations and preoperative anesthesia evaluations. METHODS: Inpatient cancellations were studied during 26 consecutive 4-week intervals between July 2012 and June 2014 at a tertiary care academic hospital. All timestamps related to scheduling, rescheduling, and cancellation activities were retrieved from the operating room (OR) case scheduling system. Timestamps when patients were seen by anesthesia residents were obtained from the preoperative evaluation system database. Batch mean methods were used to calculate means and SE. For cases cancelled, we determined whether, for "most" (>50%) cancellations, a subsequent procedure (of any type) was performed on the patient within 7 days of the cancellation. Comparisons with most and other fractions were assessed using the 1 group, 1-sided Student t test. We evaluated whether a few procedures were highly represented among the cancelled cases via the Herfindahl (Simpson's) index, comparing it with <0.15. The rate of scheduling activity was assessed by computing the number of OR scheduling office decisions in each 1-hour bin between 6:00 AM and 3:59 PM. These values were compared with ≥1 decision per hour at the study hospital. RESULTS: Data from 24,735 scheduled inpatient cases were assessed. Cases cancelled after 7 AM on the day before or at any time on the scheduled day of surgery accounted for 22.6% ± 0.5% (SE) of the scheduled minutes all scheduled cases, and 26.8% ± 0.4% of the case volume (i.e., number of cases). Most (83.1% ± 0.6%, P < 10) cases performed were evaluated on the day before surgery. Most (67.6% ± 1.6%, P < 10) minutes of cancelled cases were evaluated on the day before surgery. Most (62.3% ± 1.5%, P < 10) cases were seen earlier than 6:00 PM of the day before surgery. The Herfindahl index among cancelled procedures was 0.021 ± 0.001 (P < 10 compared not only to <0.15 but also to <0.05), showing large heterogeneity among the cancelled procedures. A subsequent procedure was not performed for most cancelled cases (50.6% ± 0.9% compared with >50%, P = 0.12), implying that the indication for the cancelled procedure no longer existed or the patient/family decided not to proceed with surgery. When only cancellations on the scheduled day of surgery were considered, the cancellation rate was 14.0% ± 0.3% of scheduled inpatient minutes and 11.8% ± 0.2% of scheduled inpatient cases. There were 0.59 ± 0.02 OR schedule decisions per hour per 10 ORs between 6:00 AM and 3:59 PM (P < 10, corresponding to ≥1 decision per hour at the 36 OR study hospital). CONCLUSIONS: The study hospital had a high inpatient cancellation rate, despite the fact that most patients whose cases were cancelled were seen by an anesthesia resident by 6:00 PM of the day before surgery. This finding suggests that further efforts to reduce the cancellations by seeing patients sooner on the day before surgery, or seeing even more patients the day before surgery, would not be an economically useful focus of the Perioperative Surgical Home. The wide heterogeneity among cancelled cases indicates that focusing on a few procedures would not materially affect the overall cancellation rate. The relatively low rate of subsequent performance of a procedure on patients whose cases had been cancelled suggests that trying to decrease the cancellation rate might be medically counterproductive. The hourly rate of decisions in the scheduling office during regular work hours on the day of surgery highlights the importance of decisions made at the OR control desk and scheduling office throughout the day to reduce the hours of overused OR time. These data suggest that efforts of the Perioperative Surgical Home related to inpatient cancellations should focus on management decision-making to mitigate the disruptions to the planned OR schedule caused by inpatient case cancellations and add-on cases, more so than on efforts to reduce inpatient cancellation rates.
Subject(s)
Anesthesia Department, Hospital/standards , Appointments and Schedules , Inpatients , Operating Room Information Systems/standards , Outcome and Process Assessment, Health Care/standards , Personnel Staffing and Scheduling Information Systems/standards , Personnel Staffing and Scheduling/standards , Quality Improvement/standards , Quality Indicators, Health Care/standards , Workload/standards , Academic Medical Centers , After-Hours Care/standards , Anesthesia Department, Hospital/organization & administration , Efficiency, Organizational , Humans , Internship and Residency/standards , Operating Room Information Systems/organization & administration , Personnel Staffing and Scheduling/organization & administration , Personnel Staffing and Scheduling Information Systems/organization & administration , Philadelphia , Task Performance and Analysis , Tertiary Care Centers , Time Factors , WorkflowABSTRACT
BACKGROUND: Many anesthetic drug errors result from vial or syringe swaps. Scanning the barcodes on vials before drug preparation, creating syringe labels that include barcodes, and scanning the syringe label barcodes before drug administration may help to prevent errors. In contrast, making syringe labels by hand that comply with the recommendations of regulatory agencies and standards-setting bodies is tedious and time consuming. A computerized system that uses vial barcodes and generates barcoded syringe labels could address both safety issues and labeling recommendations. METHODS: We measured compliance of syringe labels in multiple operating rooms (ORs) with the recommendations of regulatory agencies and standards-setting bodies before and after the introduction of the Codonics Safe Label System (SLS). The Codonics SLS was then combined with Smart Anesthesia Manager software to create an anesthesia barcode drug administration system, which allowed us to measure the rate of scanning syringe label barcodes at the time of drug administration in 2 cardiothoracic ORs before and after introducing a coffee card incentive. Twelve attending cardiothoracic anesthesiologists and the OR satellite pharmacy participated. RESULTS: The use of the Codonics SLS drug labeling system resulted in >75% compliant syringe labels (95% confidence interval, 75%-98%). All syringe labels made using the Codonics SLS system were compliant. The average rate of scanning barcodes on syringe labels using Smart Anesthesia Manager was 25% (730 of 2976) over 13 weeks but increased to 58% (956 of 1645) over 8 weeks after introduction of a simple (coffee card) incentive (P < 0.001). CONCLUSIONS: An anesthesia barcode drug administration system resulted in a moderate rate of scanning syringe label barcodes at the time of drug administration. Further, adaptation of the system will be required to achieve a higher utilization rate.
Subject(s)
Anesthesia Department, Hospital , Anesthesia , Anesthetics/administration & dosage , Drug Labeling/instrumentation , Medication Errors/prevention & control , Medication Systems, Hospital , Pharmacy Service, Hospital , Anesthesia/adverse effects , Anesthesia/methods , Anesthesia/standards , Anesthesia Department, Hospital/methods , Anesthesia Department, Hospital/standards , Anesthetics/adverse effects , Anesthetics/standards , Drug Labeling/methods , Drug Labeling/standards , Equipment Design , Equipment Failure , Guideline Adherence , Humans , Materials Testing , Medication Systems, Hospital/standards , Pharmacy Service, Hospital/methods , Pharmacy Service, Hospital/standards , Practice Guidelines as Topic , Software Design , Treatment OutcomeABSTRACT
BACKGROUND: Delays to intra-arterial therapy (IAT) lead to worse outcomes in stroke patients with proximal occlusions. Little is known regarding the magnitude of, and reasons for, these delays. In a pilot quality improvement (QI) project, we sought to examine and improve our door-puncture times. METHODS AND RESULTS: For anterior-circulation stroke patients who underwent IAT, we retrospectively calculated in-hospital time delays associated with various phases from patient arrival to groin puncture. We formulated and then implemented a process change targeted to the phase with the greatest delay. We examined the impact on time to treatment by comparing the pre- and post-QI cohorts. One hundred forty-six patients (93 pre- vs. 51 post-QI) were analyzed. In the pre-QI cohort (ie, sequential process), the greatest delay occurred from imaging to the neurointerventional (NI) suite ("picture-suite": median, 62 minutes; interquartile range [IQR], 40 to 82). A QI measure was instituted so that the NI team and anesthesiologist were assembled and the suite set up in parallel with completion of imaging and decision making. The post-QI (ie, parallel process) median picture-to-suite time was 29 minutes (IQR, 21 to 41; P<0.0001). There was a 36-minute reduction in median door-to-puncture time (143 vs. 107 minutes; P<0.0001). Parallel workflow and presentation during work hours were independent predictors of shorter door-puncture times. CONCLUSIONS: In-hospital delays are a major obstacle to timely IAT. A simple approach for achieving substantial time savings is to mobilize the NI and anesthesia teams during patient evaluation and treatment decision making. This parallel workflow resulted in a >30-minute (25%) reduction in median door-to-puncture times.