scholarly journals P141: Identifying causes of delay in interfacility transfer of patients by air ambulance

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S115
Author(s):  
A. Wong ◽  
A. McParland ◽  
B. Nolan
Keyword(s):  
Chart Review ◽  
Free Text ◽  
Transport Time ◽  
Trip Planning ◽  
Air Ambulance ◽  
Documentation Quality ◽  
Common Causes ◽  
Total Transport ◽  
Interfacility Transport ◽  
Manual Review

Introduction: Vast geography and low population density limit availability of specialized trauma and medical care in many areas of Ontario. As such, patients with severe illnesses often require a higher level of care than local facilities can provide and thus require an interfacility transfer to access tertiary or quaternary care. In Ontario, Ornge, a provincially run air ambulance, serves as the sole provider of air-based medical and critical care transport. Patient outcomes are impacted by the time to definitive care, yet little research about reasons for delay in interfacility transfer within Ontario has been conducted. This study aimed to identify causes of delay in interfacility transport by air ambulance in Ontario. Methods: Causes of delay were identified by manual chart review of electronic patient care records (ePCR). All emergent adult interfacility transfers for patients transported by Ornge between Jan. 1-Dec. 31, 2016 were eligible for inclusion. Patient records were flagged to be manually reviewed if they met one or more of the following criteria: 1) contained a standardized delay code; 2) the ePCR free text contained “delay”, “wait”, “duty-out”, or common misspellings therein; 3) were above the 75th percentile in total transport time; or 4) were above the 90th percentile in time to patient bedside, time spent at the sending hospital, or time to receiving facility. Each trip was categorized as having delays that fall into one or more of the following categories: time-to-sending delays, in-hospital delays, and time-to-receiving/handover delays. Results: Our search strategy identified 1,220 records for manual review and a total of 872 delays were identified. The most common delays cited included aircraft refuelling (234 delays); waiting for land EMS escort (144); and unstable patients requiring advanced care such as intubation, procedures, or transfusion (79). Other delays included handover or delays at the receiving facility (42); mechanical issues (36); dispatch-related issues (53); environmental hazards (43); staffing issues (47); and equipment problems (38). Conclusion: Some common causes of interfacility delay are potentially modifiable: better trip planning around refueling, and improved coordination with local EMS could impact many delayed interfacility trips in Ontario. Our analysis was limited by number and completeness of available records, and documentation quality. To better understand causes for delay, we would benefit from improved documentation and record availability.

Identifying causes of delay in interfacility transfer of patients by air ambulance

CJEM ◽  
2020 ◽  
Vol 22 (S2) ◽  
pp. S30-S37
Author(s):  
Alanna Wong ◽  
Aidan McParland ◽  
Brodie Nolan
Keyword(s):  
Free Text ◽  
Transport Time ◽  
Trip Planning ◽  
Air Ambulance ◽  
Common Causes ◽  
Specialized Care ◽  
Total Transport ◽  
Medical Transport ◽  
Interfacility Transport ◽  
Manual Review

ABSTRACTObjectivesPopulation density can limit the level of care that can be provided in local facilities in Ontario, and as such, patients with severe illnesses often require interfacility transfers to access specialized care. This study aimed to identify causes of delay in interfacility transport by air ambulance in Ontario.MethodsCauses of delay were identified by manual review of electronic patient care records (ePCRs). All emergent interfacility transfers conducted by Ornge, the sole provider of air-based medical transport in Ontario, between January 1, 2016 and December 31, 2016 were included. The ePCRs were reviewed if they met one or more of the following: (1) contained a standardized delay code; (2) contained free text including “delay”, “wait”, or “duty-out”; (3) were above the 75th percentile in total transport time; or (4) were above the 90th percentile in time to bedside, time at the sending hospital, or time to receiving facility.ResultsOur search strategy identified 1,220 ePCRs for manual review, which identified a total of 872 delays. Common delays cited included aircraft refueling (234 delays), waiting for land emergency medical service (EMS) escort (146), and staffing- or dispatch-related issues (124). Other delays included weather/environmental hazards (43); mechanical issues (36); and procedures, imaging, or stabilization (80).ConclusionsSome common causes of interfacility delay are potentially modifiable: better trip planning around refueling and improved coordination with local EMS, could reduce delays experienced during interfacility trips. To better understand causes of delay, we would benefit from improved documentation and record availability which limited the results in this study.

scholarly journals Measuring Adoption of Patient Priorities-Aligned Care Using Natural Language Processing

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 183-183
Author(s):  
Javad Razjouyan ◽  
Jennifer Freytag ◽  
Edward Odom ◽  
Lilian Dindo ◽  
Aanand Naik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chart Review ◽  
Group Analysis ◽  
Intervention Group ◽  
Multiple Chronic Conditions ◽  
Free Text ◽  
Term Care

Abstract Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults with multiple chronic conditions. Social workers (SW), after online training, document PPC in the patient’s electronic health record (EHR). Our goal is to identify free-text notes with PPC language using a natural language processing (NLP) model and to measure PPC adoption and effect on long term services and support (LTSS) use. Free-text notes from the EHR produced by trained SWs passed through a hybrid NLP model that utilized rule-based and statistical machine learning. NLP accuracy was validated against chart review. Patients who received PPC were propensity matched with patients not receiving PPC (control) on age, gender, BMI, Charlson comorbidity index, facility and SW. The change in LTSS utilization 6-month intervals were compared by groups with univariate analysis. Chart review indicated that 491 notes out of 689 had PPC language and the NLP model reached to precision of 0.85, a recall of 0.90, an F1 of 0.87, and an accuracy of 0.91. Within group analysis shows that intervention group used LTSS 1.8 times more in the 6 months after the encounter compared to 6 months prior. Between group analysis shows that intervention group has significant higher number of LTSS utilization (p=0.012). An automated NLP model can be used to reliably measure the adaptation of PPC by SW. PPC seems to encourage use of LTSS that may delay time to long term care placement.

scholarly journals Algorithm to detect pediatric provider attention to high BMI and associated medical risk

2018 ◽  
Vol 26 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Christy B Turer ◽  
Celette S Skinner ◽  
Sarah E Barlow
Keyword(s):  
Body Mass Index ◽  
Electronic Health Record ◽  
Chart Review ◽  
Health Record ◽  
Medical Risk ◽  
Practice Behaviors ◽  
Diagnosis Codes ◽  
Manual Review ◽  
Clinician Practice ◽  
High Bmi

Abstract We developed and validated an algorithm that uses combinations of extractable electronic-health-record (EHR) indicators (diagnosis codes, orders for laboratories, medications, and referrals) that denote widely-recommended clinician practice behaviors: attention to overweight/obesity/body mass index alone (BMI Alone), with attention to hypertension/other comorbidities (BMI/Medical Risk), or neither (No Attention). Data inputs used for each EHR indicator were refined through iterative chart review to identify and resolve modifiable coding errors. Validation was performed through manual review of randomly selected visit encounters (n = 308) coded by the refined algorithm. Of 104 encounters coded as No Attention, 89.4% lacked any evidence (specificity) of attention to BMI/Medical Risk. Corresponding evidence (sensitivity) of attention to BMI Alone was identified in 96.0% (of 101 encounters coded as BMI Alone) and BMI/Medical Risk in 96.1% (of 103 encounters coded as BMI/Medical Risk). Our EHR data algorithm can validly determine provider attention to BMI alone, with Medical Risk, or neither.

Assessing the Difficulty and Time Cost of De-identification in Clinical Narratives

2006 ◽  
Vol 45 (03) ◽  
pp. 246-252 ◽  
Author(s):  
W. F. Phillips ◽  
S. Phansalkar ◽  
S. A. Sims ◽  
J. F. Hurdle ◽  
D. A. Dorr
Keyword(s):  
Regular Expression ◽  
Free Text ◽  
Time Cost ◽  
Clinical Notes ◽  
The Us ◽  
Important Challenge ◽  
Automated Technique ◽  
Manual Review ◽  
Simple Search ◽  
Selection Of

Summary Objective: To characterize the difficulty confronting investigators in removing protected health information (PHI) from cross-discipline, free-text clinical notes, an important challenge to clinical informatics research as recalibrated by the introduction of the US Health Insurance Portability and Accountability Act (HIPAA) and similar regulations. Methods: Randomized selection of clinical narratives from complete admissions written by diverse providers, reviewed using a two-tiered rater system and simple automated regular expression tools. For manual review, two independent reviewers used simple search and replace algorithms and visual scanning to find PHI as defined by HIPAA, followed by an independent second review to detect any missed PHI. Simple automated review was also performed for the “easy” PHI that are number- or date-based. Results: From 262 notes, 2074 PHI, or 7.9 ± 6.1 per note, were found. The average recall (or sensitivity) was 95.9% while precision was 99.6% for single reviewers. Agreement between individual reviewers was strong (ICC = 0.99), although some asymmetry in errors was seen between reviewers (p = 0.001). The automated technique had better recall (98.5%) but worse precision (88.4%) for its subset of identifiers. Manually de-identifying a note took 87.3 ± 61 seconds on average. Conclusions: Manual de-identification of free-text notes is tedious and time-consuming, but even simple PHI is difficult to automatically identify with the exactitude required under HIPAA.

scholarly journals P043: Outcomes associated with prehospital refractory ventricular fibrillation

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S92
Author(s):  
M. Davis ◽  
A. Schappert ◽  
B. Chau ◽  
A. Leung ◽  
K. Van Aarsen
Keyword(s):  
Ventricular Fibrillation ◽  
Treatment Strategies ◽  
Spontaneous Circulation ◽  
Transport Time ◽  
Prehospital Treatment ◽  
Shockable Rhythm ◽  
History Of ◽  
Artery Disease ◽  
Hospital Cardiac Arrest ◽  
Manual Review

Introduction: When ventricular fibrillation (VF) cannot be terminated with conventional external defibrillation, it is classified as refractory VF (RVF). There is a paucity of information regarding prehospital or patient factors that may be associated with RVF. The objectives of this study were to determine factors that may be associated with RVF, the initial ED rhythm for patients with prehospital RVF, and the incidence of survival in patients who had RVF and were transported to hospital. Methods: Ambulance Call Records (ACRs) of patients with out of hospital cardiac arrest between Mar. 1 2012 and Apr. 1 2016 were reviewed. Cases of RVF (≥5 consecutive shocks delivered) were determined by manual review of the ACR. ED and hospital records were analyzed to determine outcomes of patients who were in RVF and transported to hospital. Descriptive statistics were calculated and all variables were tested for an association with initial ED rhythm, survival to admission, and survival to discharge. Results: Eighty-five cases of RVF were identified. A history of coronary artery disease (47.10%) and hypertension (50.60%) were the most common comorbidities in patients transported to the ED with RVF. Upon arrival to the ED, 24 (28.2%) remained in RVF, 38 (44.7%) had a non-shockable rhythm, and 23 (27.1%) had return of spontaneous circulation. Thirty-four (40%) survived to admission, while only 18 (21.2%) survived to discharge. Pre-existing comorbidities, time to first shock, time on scene, and transport time were not statistically associated with initial ED rhythm, survival to admission or discharge. Patient age was statistically associated with improved rhythm on ED arrival (p=0.013) and survival to discharge (58.24 yrs vs 67.40 yrs, Δ9.17, 95% CI 1.82 to 16.52, p=0.015). Conclusion: The majority of patients with prehospital RVF have a rhythm deterioration by the time care is transferred to the ED. Of these patients with a rhythm deterioration, few survive to hospital discharge. Younger patients are more likely to remain in RVF and survive to discharge. Further research is required to determine prehospital treatment strategies for RVF, as well as patient populations that may benefit from those treatments.

scholarly journals Geointelligence against Illegal Deforestation and Timber Laundering in the Brazilian Amazon

2020 ◽  
Vol 9 (6) ◽  
pp. 398 ◽  
Author(s):  
Franco Perazzoni ◽  
Paula Bacelar-Nicolau ◽  
Marco Painho
Keyword(s):  
Evaluation Criteria ◽  
Brazilian Amazon ◽  
Benford’S Law ◽  
Transport Time ◽  
Clear Cutting ◽  
Forest Inventories ◽  
Brazilian Legislation ◽  
Benford's Law ◽  
Total Transport ◽  
Impossible Task

Due to the characteristics of the Southern Amazonas Mesoregion (Mesorregião Sul do Amazonas, MSA), conducting on-site surveys in all licensed forestry areas (Plano de Manejo Florestal, PMFS) is an impossible task. Therefore, the present investigation aimed to: (i) analyze the use of geointelligence (GEOINT) techniques to support the evaluation of PMFS; and (ii) verify if the PMFS located in the MSA are being executed in accordance with Brazilian legislation. A set of twenty-two evaluation criteria were established. These were initially applied to a “standard” PMFS and subsequently replicated to a larger area of 83 PMFS, located in the MSA. GEOINT allowed for a better understanding of each PMFS, identifying illegal forestry activities and evidence of timber laundering. Among these results, we highlight the following evidences: (i) inconsistencies related to total transport time and prices declared to the authorities (70% of PMFS); (ii) volumetric information incompatible with official forest inventories and/or not conforming with Benford’s law (54% of PMFS); (iii) signs of exploitation outside the authorized polygon limits (51% of PMFS) and signs of clear-cutting (43% of PMFS); (iv) no signs of infrastructure compatible with licensed forestry (24% of PMFS); and (v) signs of exploitation prior to the licensing (19% of PMFS) and after the expiration of licensing (5%).

Patterns of Early Mortality after Trauma in a Neighborhood Urban Trauma Center: Can We Improve Outcomes?

2013 ◽  
Vol 79 (8) ◽  
pp. 764-767 ◽  
Author(s):  
Lindsay Berbiglia ◽  
Peter P. Lopez ◽  
Leah Bair ◽  
Adelaide Ammon ◽  
Gwyneth Navas ◽  
...  
Keyword(s):  
Chart Review ◽  
Injury Severity ◽  
Retrospective Chart Review ◽  
Gunshot Wounds ◽  
Common Mechanism ◽  
Trauma Patients ◽  
Hospital Arrival ◽  
Common Causes ◽  
Care Improvement ◽  
Uncontrolled Hemorrhage

Even with specialized trauma systems, a significant number of deaths occur within the early postinjury period. Our goal was to examine deaths within this period for cause and determine if care could improve outcomes. A retrospective chart review was performed on all patients who were dead on arrival or died within 4 hours of arrival between January 1, 2005, and December 31, 2011. Survival probabilities and Injury Severity Score (ISS) were calculated. Chart review and trauma review processes were used to determine cases with opportunities for care improvement. Two hundred eighty-nine patients were dead on arrival (DOA), and 176 patients died within 4 hours of arrival. The most common mechanism of injury was gunshot wounds (68.4%). The most common causes of death were uncontrolled hemorrhage (68.2%) and neurologic trauma (23.4%). Average ISS was 32. Twenty-nine patients had survival probability percentages over 50. Ten of 176 (5.7%) deaths were found to have opportunities for care improvement. In three cases (1.7%), errors contributed to death. The majority of trauma patients DOA or dying within 4 hours of hospital arrival have nonsurvivable injuries. Regular trauma review processes are invaluable in determining opportunities for care improvement. Autopsy information increases the reliability of the review process.

scholarly journals Validation of a Surgical Site Infection Detection Algorithm for Use in Cardiac and Orthopedic Surgery Research

2020 ◽  
Vol 41 (S1) ◽  
pp. s55-s56
Author(s):  
Hiroyuki Suzuki ◽  
Erin Balkenende ◽  
Eli Perencevich ◽  
Gosia Clore ◽  
Kelly Richardson ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Orthopedic Surgery ◽  
False Positive ◽  
Chart Review ◽  
Improvement Program ◽  
Data Set ◽  
Sensitivity Specificity ◽  
Va Hospitals ◽  
The Impact ◽  
Manual Review

Background: Studies of interventions to decrease rates of surgical site infections (SSIs) must include thousands of patients to be statistically powered to demonstrate a significant reduction. Therefore, it is important to develop methodology to extract data available in the electronic medical record (EMR) to accurately measure SSI rates. Prior studies have created tools that optimize sensitivity to prioritize chart review for infection control purposes. However, for research studies, positive predictive value (PPV) with reasonable sensitivity is preferred to limit the impact of false-positive results on the assessment of intervention effectiveness. Using information from the prior tools, we aimed to determine whether an algorithm using data available in the Veterans Affairs (VA) EMR could accurately and efficiently identify deep incisional or organ-space SSIs found in the VA Surgical Quality Improvement Program (VASQIP) data set for cardiac and orthopedic surgery patients. Methods: We conducted a retrospective cohort study of patients who underwent cardiac surgery or total joint arthroplasty (TJA) at 11 VA hospitals between January 1, 2007, and April 30, 2017. We used EMR data that were recorded in the 30 days after surgery on inflammatory markers; microbiology; antibiotics prescribed after surgery; International Classification of Diseases (ICD) and current procedural terminology (CPT) codes for reoperation for an infection related purpose; and ICD codes for mediastinitis, prosthetic joint infection, and other SSIs. These metrics were used in an algorithm to determine whether a patient had a deep or organ-space SSI. Sensitivity, specificity, PPV and negative predictive values (NPV) were calculated for accuracy of the algorithm through comparison with 30-day SSI outcomes collected by nurse chart review in the VASQIP data set. Results: Among the 11 VA hospitals, there were 18,224 cardiac surgeries and 16,592 TJA during the study period. Of these, 20,043 were evaluated by VASQIP nurses and were included in our final cohort. Of the 8,803 cardiac surgeries included, manual review identified 44 (0.50%) mediastinitis cases. Of the 11,240 TJAs, manual review identified 71 (0.63%) deep or organ-space SSIs. Our algorithm identified 32 of the mediastinitis cases (73%) and 58 of the deep or organ-space SSI cases (82%). Sensitivity, specificity, PPV, and NPV are shown in Table 1. Of the patients that our algorithm identified as having a deep or organ-space SSI, only 21% (PPV) actually had an SSI after cardiac surgery or TJA. Conclusions: Use of the algorithm can identify most complex SSIs (73%–82%), but other data are necessary to separate false-positive from true-positive cases and to improve the efficiency of case detection to support research questions.Funding: NoneDisclosures: None

scholarly journals Unsuccessful Percutaneous Endoscopic Lumbar Discectomy

Neurosurgery ◽  
2015 ◽  
Vol 76 (4) ◽  
pp. 372-381 ◽  
Author(s):  
Kyung-Chul Choi ◽  
June-Ho Lee ◽  
Jin-Sung Kim ◽  
Luigi Andrew Sabal ◽  
Sol Lee ◽  
...  
Keyword(s):  
Chart Review ◽  
Lumbar Discectomy ◽  
Herniated Disc ◽  
Percutaneous Endoscopic Lumbar Discectomy ◽  
Primary Surgery ◽  
Incomplete Removal ◽  
Common Causes ◽  
Surgical Failure ◽  
Endoscopic Lumbar Discectomy ◽  
Postoperative Findings

Abstract BACKGROUND: Percutaneous endoscopic lumbar discectomy (PELD) has remarkably evolved with successful results. Although PELD has gained popularity for the treatment of herniated disc (HD), the risk of surgical failure may be a major obstacle to performing PELD. We analyzed unsuccessful cases requiring reoperation. OBJECTIVE: To find common causes of surgical failure and elucidate the limitations of the conventional PELD technique. METHODS: A retrospective review was performed on all patients who had undergone PELD between January 2001 and December 2012. Unsuccessful PELD was defined as a case requiring reoperation within 6 weeks after primary surgery. Chart review was done, and preoperative, intraoperative, and postoperative radiographic reviews were performed. All unsuccessful PELD cases were classified according to the type of HD, location of herniation, extruded disc migration, working channel position, and intraoperative and postoperative findings. RESULTS: In 12 years, 10 228 patients had undergone PELD; 436 (4.3%) cases were unsuccessful. The causes were incomplete removal of HDs in 283 patients (2.8%), recurrence in 78 (0.8%), persistent pain even after complete HD removal in 41 (0.4%), and approach-related pain in 21 (0.2%). Incomplete removal of the HD was caused by inappropriate positioning (95 cases; 33.6%) of the working channel and occurred in central HDs (91 cases; 32.2%), migrated HDs (70 cases; 24.7%), and axillary type HDs (63 cases; 22.3%). CONCLUSION: Proper surgical indications and good working channel position are important for successful PELD. PELD techniques should be specifically designed to remove the disc fragments in various types of HD.

scholarly journals Identification of asthma control factor in clinical notes using a hybrid deep learning model

2021 ◽  
Vol 21 (S7) ◽  
Author(s):  
Bhavani Singh Agnikula Kshatriya ◽  
Elham Sagheb ◽  
Chung-Il Wi ◽  
Jungwon Yoon ◽  
Hee Yun Seol ◽  
...  
Keyword(s):  
Deep Learning ◽  
Asthma Control ◽  
Chart Review ◽  
Original Data ◽  
Free Text ◽  
Model Errors ◽  
Asthma Diagnosis ◽  
Rule Based ◽  
Clinical Notes ◽  
Distant Supervision

Abstract Background There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician’s documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text. Methods The study data consist of two sets: (1) manual chart reviewed data—1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)—27,363 clinical notes from 800 patients with asthma diagnosis. A context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) was developed to identify inhaler techniques in EHR free text. Both original BERT and clinical BioBERT (cBERT) were applied with a cost-sensitivity to deal with imbalanced data. The distant supervision using weak labels by rules was also incorporated to augment the training set and alleviate a costly manual labeling process in the development of a deep learning algorithm. A hybrid approach using post-hoc rules was also explored to fix BERT model errors. The performance of BERT with/without distant supervision, hybrid, and rule-based models were compared in precision, recall, F-score, and accuracy. Results The BERT models on the original data performed similar to a rule-based model in F1-score (0.837, 0.845, and 0.838 for rules, BERT, and cBERT, respectively). The BERT models with distant supervision produced higher performance (0.853 and 0.880 for BERT and cBERT, respectively) than without distant supervision and a rule-based model. The hybrid models performed best in F1-score of 0.877 and 0.904 over the distant supervision on BERT and cBERT. Conclusions The proposed BERT models with distant supervision demonstrated its capability to identify inhaler techniques in EHR free text, and outperformed both the rule-based model and BERT models trained on the original data. With a distant supervision approach, we may alleviate costly manual chart review to generate the large training data required in most deep learning-based models. A hybrid model was able to fix BERT model errors and further improve the performance.

Sign in / Sign up

Export Citation Format

Share Document