Glucose meter standardization across a large academic hospital system.

BACKGROUND: To select and standardize point-of-care (POC) glucose meters across a multi-hospital system. METHODS: We formed a multidisciplinary POC glucose standardization working group including key stakeholders from each site. A set of selection criteria: usability, clinical and laboratory performance, indications for use, interface connectivity, ease of implementation and ongoing operational costs were used to develop a scoring schemato facilitate a consensus-driven selection process. RESULTS: Method comparison and consensus error grid evaluation against the clinically validated reference methods demonstrated that the analytical performance for all candidate meters was comparable for both the laboratory and clinical evaluation. However, Meter 1 ranked highest in usability evaluations, implementation and streamlined interface connectivity. The meter selection process and implementation were staggered across sites due to complexity of transitioning to a new manufacturer's meter and limitations in vendor support for training and ongoing troubleshooting of interface connectivity. CONCLUSIONS: Standardization of POC glucose meters in a large multi-hospital system is a complex undertaking requiring robust, multidisciplinary organizational structure both system-wide and locally, development of consensus-driven selection tools, usability evaluation by end-users, laboratory and clinical evaluation of the analytical performance, and a strong vendor-laboratory partnership during the implementation process.

Top ten challenges when interfacing a laboratory information system to an electronic health record: Experience at a large academic medical center.

BACKGROUND: Recent U.S. government regulations incentivize implementation of an electronic health record (EHR) with computerized order entry and structured results display. Many institutions have also chosen to interface their EHR to their laboratory information system (LIS). Reported long-term benefits include increased efficiency and improved quality and safety. In order to successfully implement an interfaced EHR-LIS, institutions must plan years in advance and anticipate the impact of an integrated system. It can be challenging to fully understand the technical, workflow and resource aspects and adequately prepare for a potentially protracted system implementation and the subsequent stabilization. OBJECTIVES: We describe the top ten challenges that we encountered in our clinical laboratories following the implementation of an interfaced EHR-LIS and offer suggestions on how to overcome these challenges. METHODS: This study was performed at a 777-bed, tertiary care center which recently implemented an interfaced EHR-LIS. Challenges were recorded during EHR-LIS implementation and stabilization and the authors describe the top ten. RESULTS: Our top ten challenges were selection and harmonization of test codes, detailed training for providers on test ordering, communication with EHR provider champions during the build process, fluid orders and collections, supporting specialized workflows, sufficient reports and metrics, increased volume of inpatient venipunctures, adequate resources during stabilization, unanticipated changes to laboratory workflow and ordering specimens for anatomic pathology. A few suggestions to overcome these challenges include regular meetings with clinical champions, advanced considerations of reports and metrics that will be needed, adequate training of laboratory staff on new workflows in the EHR and defining all tests including anatomic pathology in the LIS. CONCLUSION: EHR-LIS implementations have many challenges requiring institutions to adapt and develop new infrastructures. This article should be helpful to other institutions facing or undergoing a similar endeavor.

The Benefits and Challenges of an Interfaced Electronic Health Record and Laboratory Information System: Effects on Laboratory Processes.

CONTEXT: - A recent government regulation incentivizes implementation of an electronic health record (EHR) with computerized order entry and structured results display. Many institutions have also chosen to interface their EHR with their laboratory information system (LIS). OBJECTIVE: - To determine the impact of an interfaced EHR-LIS on laboratory processes. DESIGN: - We analyzed several different processes before and after implementation of an interfaced EHR-LIS: the turnaround time, the number of stat specimens received, venipunctures per patient per day, preanalytic errors in phlebotomy, the number of add-on tests using a new electronic process, and the number of wrong test codes ordered. Data were gathered through the LIS and/or EHR. RESULTS: - The turnaround time for potassium and hematocrit decreased significantly (P = .047 and P = .004, respectively). The number of stat orders also decreased significantly, from 40% to 7% for potassium and hematocrit, respectively (P < .001 for both). Even though the average number of inpatient venipunctures per day increased from 1.38 to 1.62 (P < .001), the average number of preanalytic errors per month decreased from 2.24 to 0.16 per 1000 specimens (P < .001). Overall there was a 16% increase in add-on tests. The number of wrong test codes ordered was high and it was challenging for providers to correctly order some common tests. CONCLUSIONS: - An interfaced EHR-LIS significantly improved within-laboratory turnaround time and decreased stat requests and preanalytic phlebotomy errors. Despite increasing the number of add-on requests, an electronic add-on process increased efficiency and improved provider satisfaction. Laboratories implementing an interfaced EHR-LIS should be cautious of its effects on test ordering and patient venipunctures per day.

Significant Reduction in Preanalytical Errors for Nonphlebotomy Blood Draws After Implementation of a Novel Integrated Specimen Collection Module.

OBJECTIVES: Most preanalytical errors at our institution occur during nonphlebotomy blood draws. We implemented an electronic health record (EHR), interfaced the EHR to the laboratory information system, and designed a new specimen collection module. We studied the effects of the new system on nonphlebotomy preanalytical errors. METHODS: We used an electronic database of preanalytical errors and calculated the number and type of the most common errors in the emergency department (ED) and inpatient nursing for 3-month periods before (August-October 2014) and after (August-October 2015) implementation. The level of staff compliance with the new system was also assessed. RESULTS: The average monthly preanalytical errors decreased significantly from 7.95 to 1.45 per 1,000 specimens in the ED (P < 0001) and 11.75 to 3.25 per 1,000 specimens in inpatient nursing (P < 0001). The rate of decrease was similar for mislabeled, unlabeled, wrong specimen received and no specimen received errors. Most residual errors (80% in the ED and 67% in inpatient nursing) occurred when providers did not use the new system as designed. CONCLUSIONS: Implementation of a customized specimen collection module led to a significant reduction in preanalytical errors. Improved compliance with the system may lead to further reductions in error rates.

Optimizing outpatient phlebotomy staffing: tools to assess staffing needs and monitor effectiveness.

CONTEXT: Short patient wait times are critical for patient satisfaction with outpatient phlebotomy services. Although increasing phlebotomy staffing is a direct way to improve wait times, it may not be feasible or appropriate in many settings, particularly in the context of current economic pressures in health care. OBJECTIVE: To effect sustainable reductions in patient wait times, we created a simple, data-driven tool to systematically optimize staffing across our 14 phlebotomy sites with varying patient populations, scope of service, capacity, and process workflows. DESIGN: We used staffing levels and patient venipuncture volumes to derive the estimated capacity, a parameter that helps predict the number of patients a location can accommodate per unit of time. We then used this parameter to determine whether a particular phlebotomy site was overstaffed, adequately staffed, or understaffed. Patient wait-time and satisfaction data were collected to assess the efficacy and accuracy of the staffing tool after implementing the staffing changes. RESULTS: In this article, we present the applications of our approach in 1 overstaffed and 2 understaffed phlebotomy sites. After staffing changes at previously understaffed sites, the percentage of patients waiting less than 10 minutes ranged from 88% to 100%. At our previously overstaffed site, we maintained our goal of 90% of patients waiting less than 10 minutes despite staffing reductions. All staffing changes were made using existing resources. CONCLUSIONS: Used in conjunction with patient wait-time and satisfaction data, our outpatient phlebotomy staffing tool is an accurate and flexible way to assess capacity and to improve patient wait times.

Using Lean principles to optimise inpatient phlebotomy services.

BACKGROUND: In the USA, inpatient phlebotomy services are under constant operational pressure to optimise workflow, improve timeliness of blood draws, and decrease error in the context of increasing patient volume and complexity of work. To date, the principles of Lean continuous process improvement have been rarely applied to inpatient phlebotomy. AIMS: To optimise supply replenishment and cart standardisation, communication and workload management, blood draw process standardisation, and rounding schedules and assignments using Lean principles in inpatient phlebotomy services. METHODS: We conducted four Lean process improvement events and implemented a number of interventions in inpatient phlebotomy over a 9-month period. We then assessed their impact using three primary metrics: (1) percentage of phlebotomists drawing their first patient by 05:30 for 05:00 rounds, (2) percentage of phlebotomists completing 08:00 rounds by 09:30, and (3) number of errors per 1000 draws. RESULTS: We saw marked increases in the percentage of phlebotomists drawing their first patient by 05:30, and the percentage of phlebotomists completing rounds by 09:30 postprocess improvement. A decrease in the number of errors per 1000 draws was also observed. CONCLUSIONS: This study illustrates how continuous process improvement through Lean can optimise workflow, improve timeliness, and decrease error in inpatient phlebotomy. We believe this manuscript adds to the field of clinical pathology as it can be used as a guide for other laboratories with similar goals of optimising workflow, improving timeliness, and decreasing error, providing examples of interventions and metrics that can be tailored to specific laboratories with particular services and resources.

A strategy for optimizing staffing to improve the timeliness of inpatient phlebotomy collections.

CONTEXT: The timely availability of inpatient test results is a key to physician satisfaction with the clinical laboratory, and in an institution with a phlebotomy service may depend on the timeliness of blood collections. In response to safety reports filed for delayed phlebotomy collections, we applied Lean principles to the inpatient phlebotomy service at our institution. Our goal was to improve service without using additional resources by optimizing our staffing model. OBJECTIVE: To evaluate the effect of a new phlebotomy staffing model on the timeliness of inpatient phlebotomy collections. DESIGN: We compared the median time of morning blood collections and average number of safety reports filed for delayed phlebotomy collections during a 6-month preimplementation period and 5-month postimplementation period. RESULTS: The median time of morning collections was 17 minutes earlier after implementation (7:42 am preimplementation; interquartile range, 6:27-8:48 am; versus 7:25 am postimplementation; interquartile range, 6:20-8:26 am). The frequency of safety reports filed for delayed collections decreased 80% from 10.6 per 30 days to 2.2 per 30 days. CONCLUSION: Reallocating staff to match the pattern of demand for phlebotomy collections throughout the day represents a strategy for improving the performance of an inpatient phlebotomy service.

Reduction in specimen labeling errors after implementation of a positive patient identification system in phlebotomy.

Ensuring accurate patient identification is central to preventing medical errors, but it can be challenging. We implemented a bar code-based positive patient identification system for use in inpatient phlebotomy. A before-after design was used to evaluate the impact of the identification system on the frequency of mislabeled and unlabeled samples reported in our laboratory. Labeling errors fell from 5.45 in 10,000 before implementation to 3.2 in 10,000 afterward (P = .0013). An estimated 108 mislabeling events were prevented by the identification system in 1 year. Furthermore, a workflow step requiring manual preprinting of labels, which was accompanied by potential labeling errors in about one quarter of blood "draws," was removed as a result of the new system. After implementation, a higher percentage of patients reported having their wristband checked before phlebotomy. Bar code technology significantly reduced the rate of specimen identification errors.

Applying Lean/Toyota production system principles to improve phlebotomy patient satisfaction and workflow.

Our goals were to improve the overall patient experience and optimize the blood collection process in outpatient phlebotomy using Lean principles. Elimination of non-value-added steps and modifications to operational processes resulted in increased capacity to handle workload during peak times without adding staff. The result was a reduction of average patient wait time from 21 to 5 minutes, with the goal of drawing blood samples within 10 minutes of arrival at the phlebotomy station met for 90% of patients. In addition, patient satisfaction increased noticeably as assessed by a 5-question survey. The results have been sustained for 10 months with staff continuing to make process improvements.

Point-of-care (POC) versus central laboratory instrumentation for monitoring oral anticoagulation.

Point-of-care (POC) instruments employing fingerstick whole blood to monitor patients treated with warfarin are a popular alternative to complex, central laboratory coagulation analyzers utilizing citrated plasma derived from venipuncture. We investigated the accuracy of two widely utilized POC instruments for oral anticoagulation monitoring compared with a central laboratory instrument. Instrument-to-instrument variation differed for the two POC instruments, which correlated with the central laboratory instrument, but exhibited positive bias of 0.24 - 0.35 INR units. Positive bias increased as the INR values increased. We conclude that clinicians should exercise caution when interpreting results generated by POC monitors, particularly at high INR values. A high POC measurement of INR does not necessarily warrant decreasing the warfarin dose. Instead, a predefined cut-off range for high INR values generated by POC instruments should mandate confirmatory testing with central laboratory instrumentation.