Reducing turnaround time of surgical pathology reports in pathology and laboratory medicine departments.
Ontology highlight
ABSTRACT:
SUBMITTER: Alshieban S
PROVIDER: S-EPMC4693096 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Turnaround time (TAT) is one of the most noticeable signs of laboratory service and is often used as a key performance indicator of laboratory performance. This review summarises the literature regarding laboratory TAT, focusing on the different definitions, measures, expectations, published data, associations with clinical outcomes and approaches to improve TAT. It aims to provide a consolidated source of benchmarking data useful to the laboratory in setting TAT goals and to encourage introduction of TAT monitoring for continuous quality improvement. A 90% completion time (sample registration to result reporting) of <60 minutes for common laboratory tests is suggested as an initial goal for acceptable TAT.
| S-EPMC2282400 | biostudies-other
Project description:Introduction. Clinical microbiology laboratories have had to cope with an increase in the volume of tests due to the emergence of the SARS-CoV-2 virus. Short turnaround times (TATs) are important for case tracing and to help clinicians in patient management. In such a context, high-throughput systems are essential to process the bulk of the tests. Rapid tests are also required to ensure shorter TATs for urgent situations. In our laboratory, SARS-CoV-2 assays were initially implemented on our custom platform using a previously published method. The commercial cobas 6800 (Roche diagnostics) assay and the GeneXpert Xpress (Cepheid) SARS-CoV-2 assay were implemented on 24 March and 8 April 2020, respectively, as soon as available.Hypothesis/Gap Statement. Despite the abundant literature on SARS-CoV-2 assays, the articles focus mainly on the diagnostic performances. This is to our knowledge the first article that specifically studies the TAT of different assays.Aim. We aimed to describe the impact of various SARS-CoV-2 assays on the TAT at the beginning of the outbreak.Methodology. In this study, we retrospectively analysed the TAT of all SARS-CoV-2 assays performed in our centre between 24 February and 9 June, 2020.Results. We retrieved 33 900 analyses, with a median TAT of 6.25 h. TATs were highest (6.9 h) when only our custom platform was used (24 February to 24 March, 2020). They were reduced to 6.1 h when the cobas system was introduced (24 March to 8 April, 2020). The implementation of the GeneXpert further reduced the median TAT to 4.8 h (8 April to 9 June, 2020). The GeneXpert system had the shortest median TAT (1.9 h), followed by the cobas (5.5 h) and by our custom platform (6.9 h).Conclusion. This work shows that the combination of high-throughput systems and rapid tests allows the efficient processing of a large number of tests with a short TAT. In addition, the use of a custom platform allowed the quick implementation of an in-house test when commercial assays were not yet available.
| S-EPMC8289200 | biostudies-literature
Project description:The rapid identification of blood culture isolates and antimicrobial susceptibility test (AST) results play critical roles for the optimal treatment of patients with bloodstream infections. Whereas others have looked at the time to detection in automated culture systems, we examined the overall time from specimen collection to actionable test results. We examined four points of time, namely, blood specimen collection, Gram stain, organism identification (ID), and AST reports, from electronic data from 13 U.S. hospitals for the 11 most common, clinically significant organisms in septic patients. We compared the differences in turnaround times and the times from when specimens were collected and the results were reported in the 24-h spectrum. From January 2015 to June 2016, 165,593 blood specimens were collected, of which, 9.5% gave positive cultures. No matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry was used during the study period. Across the 10 common bacterial isolates (n = 6,412), the overall median (interquartile range) turnaround times were 0.80 (0.64 to 1.08), 1.81 (1.34 to 2.46), and 2.71 (2.46 to 2.99) days for Gram stain, organism ID, and AST, respectively. For all positive cultures, approximately 25% of the specimens were collected between 6:00 a.m. and 11:59 a.m. In contrast, more of the laboratory reporting times were concentrated between 6:00 a.m. and 11:59 a.m. for Gram stain (43%), organism ID (78%), and AST (82%), respectively (P?<?0.001). The overall average turnaround times from specimen collection for Gram stain, organism ID, and AST were approximately 1, 2, and 3?days, respectively. The laboratory results were reported predominantly in the morning hours. Laboratory automation and work flow optimization may play important roles in reducing the microbiology result turnaround time.
| S-EPMC6258864 | biostudies-literature
Project description:ObjectiveQuantify changes in workload in relation to the anatomic pathologist workforce.MethodsIn house pathology reports for cytology and surgical specimens from a regional hospital laboratory over a nine- year period (2011-2019) were analyzed, using custom computer code. Report length for the diagnosis+microscopic+synoptic report, number of blocks, billing classification (L86x codes), billings, national workload model (L4E 2018), regional workload model (W2Q), case count, and pathologist workforce in full-time equivalents (FTEs) were quantified. Randomly selected cases (n = 1,100) were audited to assess accuracy.ResultsThe study period had 574,093 pathology reports that could be analyzed. The coding accuracy was estimated at 95%. From 2011 to 2019: cases/year decreased 6% (66,056 to 61,962), blocks/year increased 20% (236,197 to 283,751), L4E workload units increased 23% (165,276 to 203,894), W2Q workload units increased 21% (149,841 to 181,321), report lines increased 19% (606,862 to 723,175), workforce increased 1% (30.42 to 30.77 FTEs), billings increased 13% ($6,766,927 to $7,677,109). W2Q in relation to L4E underweights work in practices with large specimens by up to a factor of 2x.ConclusionsWork by L4E for large specimens is underrated by W2Q. Reporting requirements and pathology work-up have increased workload per pathology case. Work overall has increased significantly without a commensurate workforce increase. The significant practice changes in the pathology work environment should prompt local investment in the anatomic pathology workforce.
| S-EPMC8241038 | biostudies-literature
Project description:Placement of pathology and laboratory medicine (PALM) services requires balancing efficiency (maximizing test volume) with equitable urban-rural access. We compared the association between population density (proxy for efficiency) and travel time to the closest facility (proxy for equitable access) across levels of Tanzania's public sector health system. We linked geospatial data for Tanzania from multiple sources. Data on facility locations and other geographic measures were collected from government and non-governmental databases. We classified facilities assuming increasing PALM availability by tier: (1) dispensaries, (2) health centres, (3) district hospitals and (4) regional/referral hospitals. We used the AccessMod 5 algorithm to estimate travel time to the closest facility for each tier across Tanzania with 500-m resolution. District-level average population density and travel time to the closest facility were calculated and presented using medians and interquartile ranges. Spatial correlations between these variables were estimated using the global Moran's I and bivariate Local Indicator of Spatial Autocorrelation, specifying a queen's neighbourhood matrix. Spatial analysis was restricted to 171 contiguous districts. The study included 5406 dispensaries, 675 health centres, 186 district hospitals and 37 regional/referral hospitals. District-level travel times were shortest for Tier 1 (median: [IQR]: 45.4 min [30.0-74.7]) and longest for Tier 4 facilities (160.2 min [107.3-260.0]). There was a weak spatial autocorrelation across tiers (Tier 1: -0.289, Tier 2: -0.292, Tier 3: -0.271 and Tier 4: -0.258) and few districts were classified as significant spatial outliers. Across tiers, geographic patterns of populated districts surrounded by neighbours with short travel time and sparsely populated districts surrounded by neighbours with long travel time were observed. Similar spatial correlation measures across health system levels suggest that Tanzania's health system reflects equitable urban-rural access to different PALM services. Longer travel times to hospital-based care could be ameliorated by shifting specialized diagnostics to more accessible lower tiers.
| S-EPMC8359747 | biostudies-literature
Project description:ObjectiveThe aims of this study were to describe overcrowding in regional emergency departments in Seoul, Korea and evaluate the effect of crowdedness on ambulance turnaround time.MethodsThis study was conducted between January 2010 and December 2010. Patients who were transported by 119-responding ambulances to 28 emergency centers within Seoul were eligible for enrollment. Overcrowding was defined as the average occupancy rate, which was equal to the average number of patients staying in an emergency department (ED) for 4 hours divided by the number of beds in the ED. After selecting groups for final analysis, multi-level regression modeling (MLM) was performed with random-effects for EDs, to evaluate associations between occupancy rate and turnaround time.ResultsBetween January 2010 and December 2010, 163,659 patients transported to 28 EDs were enrolled. The median occupancy rate was 0.42 (range: 0.10-1.94; interquartile range (IQR): 0.20-0.76). Overcrowded EDs were more likely to have older patients, those with normal mentality, and non-trauma patients. Overcrowded EDs were more likely to have longer turnaround intervals and traveling distances. The MLM analysis showed that an increase of 1% in occupancy rate was associated with 0.02-minute decrease in turnaround interval (95% CI: 0.01 to 0.03). In subgroup analyses limited to EDs with occupancy rates over 100%, we also observed a 0.03 minute decrease in turnaround interval per 1% increase in occupancy rate (95% CI: 0.01 to 0.05).ConclusionsIn this study, we found wide variation in emergency department crowding in a metropolitan Korean city. Our data indicate that ED overcrowding is negatively associated with turnaround interval with very small practical significance.
| S-EPMC4482653 | biostudies-literature
Project description:ImportanceInappropriate use of laboratory testing is a challenging problem. Estimated overuse rates of approximately 20% have been reported. Effective, sustainable solutions to stimulate optimal use are needed.ObjectiveTo determine the association of a multifaceted intervention with laboratory test volume.Design, setting, and participantsA before-after quality improvement study was performed between August 1, 2016, and April 30, 2018, in the internal medicine departments of 4 teaching hospitals in the Netherlands. Data on laboratory order volumes from 19 comparable hospitals were used as controls. The participants were clinicians ordering laboratory tests.InterventionsThe intervention included creating awareness through education and feedback, intensified supervision of residents, and changes in order entry systems. Interventions were performed by local project teams and guided by a central project team during a 6-month period. Sustainability was investigated during an 8-month follow-up period.Main outcomes and measuresThe primary outcome was the change in slope for laboratory test volume. Secondary outcomes were change in slope for laboratory expenditure, order volumes and expenditure for other diagnostic procedures, and clinical outcomes. Data were collected on duration of hospital stay, rate of repeated outpatient visits, 30-day readmission rate, and rate of unexpected prolonged duration of hospital stay for patients admitted for pneumonia.ResultsThe numbers of internists and residents ordering tests in hospitals 1 to 4 were 16 and 30, 18 and 20, 13 and 17, and 21 and 60, respectively. Statistically significant changes in slope for laboratory test volume per patient contact were found at hospital 1 (change in slope, -1.55; 95% CI, -1.98 to -1.11; P < .001), hospital 3 (change in slope, -0.74; 95% CI, -1.42 to -0.07; P = .03), and hospital 4 (change in slope, -2.18; 95% CI, -3.27 to -1.08; P < .001). At hospital 2, the change in slope was not statistically significant (-0.34; 95% CI, -2.27 to 1.58; P = .73). Laboratory test volume per patient contact decreased by 11.4%, whereas the volume increased by 2.4% in 19 comparable hospitals. Statistically significant changes in slopes for laboratory costs and volumes and costs for other diagnostic procedures were also observed. Clinical outcomes were not associated with negative changes. Important facilitators were education, continuous attention for overuse, feedback, and residents' involvement. Important barriers were difficulties in data retrieval, difficulty in incorporation of principles in daily practice, and high resident turnover.Conclusions and relevanceA set of interventions aimed at changing caregivers' mindset was associated with a reduction in the laboratory test volume in all departments, whereas the volume increased in comparable hospitals in the Netherlands. This study provides a framework for nationwide implementation of interventions to reduce unnecessary laboratory testing.
| S-EPMC6659142 | biostudies-literature
Project description:The use of some nucleic acid amplification tests (NAATs) for the diagnosis of group A Streptococcus (GAS) pharyngitis allows laboratories to adopt single-tiered testing without reflex culture. However, centralization may delay the delivery of actionable information to the bedside, particularly in the outpatient setting. We describe two novel workflows at our institution and their effect on in-lab turnaround time (TAT) at a tertiary care microbiology lab. Laboratory records were extracted, and relevant data were analyzed after the implementation of qualitative in vitro diagnostic testing for GAS with the Xpert Xpress Strep A assay, performed using the GeneXpert Infinity-48s. Workflow optimization steps studied included: (i) direct specimen submission to the microbiology laboratory via the pneumatic tube system and (ii) autoverification of GAS NAAT results in the laboratory information system. Between April 2018 and October 2018, 2,595 unique specimens were tested for GAS by PCR. Of these, 2,523 were included in the final analysis. Linear regression established that the total in-lab TAT was significantly reduced by direct specimen submission to the microbiology laboratory, autoverification, and processing during the night shift. We describe two workflow optimization methods that reduced the in-lab TAT for GAS NAAT. Although microbiology labs historically use manual processes, the advent of total laboratory automation and the adoption of on-demand NAATs will allow for more streamlined processing of microbiology specimens. It may be beneficial to consider instrument interfacing and specimen processing optimization during the early phases of implementation planning for NAATs in the microbiology laboratory.
| S-EPMC6711925 | biostudies-literature
Project description:OBJECTIVES:To assess which osmolarity equation best predicts directly measured serum/plasma osmolality and whether its use could add value to routine blood test results through screening for dehydration in older people. DESIGN:Diagnostic accuracy study. PARTICIPANTS:Older people (?65?years) in 5 cohorts: Dietary Strategies for Healthy Ageing in Europe (NU-AGE, living in the community), Dehydration Recognition In our Elders (DRIE, living in residential care), Fortes (admitted to acute medical care), Sjöstrand (emergency room) or Pfortmueller cohorts (hospitalised with liver cirrhosis). REFERENCE STANDARD FOR HYDRATION STATUS:Directly measured serum/plasma osmolality: current dehydration (serum osmolality>300?mOsm/kg), impending/current dehydration (?295?mOsm/kg). INDEX TESTS:39 osmolarity equations calculated using serum indices from the same blood draw as directly measured osmolality. RESULTS:Across 5 cohorts 595 older people were included, of whom 19% were dehydrated (directly measured osmolality>300?mOsm/kg). Of 39 osmolarity equations, 5 showed reasonable agreement with directly measured osmolality and 3 had good predictive accuracy in subgroups with diabetes and poor renal function. Two equations were characterised by narrower limits of agreement, low levels of differential bias and good diagnostic accuracy in receiver operating characteristic plots (areas under the curve>0.8). The best equation was osmolarity=1.86×(Na++K+)+1.15×glucose+urea+14 (all measured in mmol/L). It appeared useful in people aged ?65?years with and without diabetes, poor renal function, dehydration, in men and women, with a range of ages, health, cognitive and functional status. CONCLUSIONS:Some commonly used osmolarity equations work poorly, and should not be used. Given costs and prevalence of dehydration in older people we suggest use of the best formula by pathology laboratories using a cutpoint of 295?mOsm/L (sensitivity 85%, specificity 59%), to report dehydration risk opportunistically when serum glucose, urea and electrolytes are measured for other reasons in older adults. TRIAL REGISTRATION NUMBERS:DRIE: Research Register for Social Care, 122273; NU-AGE: ClinicalTrials.gov NCT01754012.
| S-EPMC4636668 | biostudies-literature
Project description:METHODS:We evaluated therapeutic TAT for a tertiary hospital in Western Kenya, using a time-motion study focusing specifically on common hematology and biochemistry orders. The aim was to determine significant bottlenecks in diagnostic testing processes at the institution. RESULTS:A total of 356 (155 hematology and 201 biochemistry) laboratory tests were fully tracked from the time of ordering to availability of results to care providers. The total therapeutic TAT for all tests was 21.5 ± 0.249 hours (95% CI). The therapeutic TAT for hematology was 20.3 ± 0.331 hours (95% CI) while that for biochemistry tests was 22.2 ± 0.346 hours (95% CI). Printing, sorting and dispatch of the printed results emerged as the most significant bottlenecks, accounting for up to 8 hours of delay (Hematology-8.3 ± 1.29 hours (95% CI), Biochemistry-8.5 ± 1.18 hours (95% CI)). Time of test orders affected TAT, with orders made early in the morning and those in the afternoon experiencing the most delays in TAT. CONCLUSION:Significant inefficiencies exist at multiple steps in the turnaround times for routine laboratory tests at a large referral hospital within an LMIC setting. Multiple opportunities exist to improve TAT and streamline processes around diagnostic testing in this and other similar settings.
| S-EPMC7141613 | biostudies-literature