scholarly journals Opioid Overdose Ambulance Runs: How Wisconsin Uses Free Text Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashley Bergeron ◽  
Jennifer Broad ◽  
Dr. Ousmane Diallo ◽  
Gary Raol ◽  
Milda Aksamitauskas
Keyword(s):  
False Positive ◽  
Population Level ◽  
Case Definition ◽  
Data System ◽  
Opioid Overdose ◽  
Free Text ◽  
Regular Expressions ◽  
Data Systems ◽  
Case Definitions ◽  
Emergency Ambulance

Objective: 1. Develop an understanding of the benefits and challenges of analyzing free text fields on a population level.2. Observe how a complex surveillance definition can be created from free text fields.3. Observe how an ambulance data system can be used to describe the opioid epidemic.Introduction: In 2016, twelve states received Center for Disease Control and Prevention (CDC) Enhanced State Opioid Overdose Surveillance grants. The purpose of the grant is to explore enhanced data sources to track nonfatal opioid overdoses. One data source is ambulance runs. Wisconsin collects ambulance run information within the Wisconsin Ambulance Runs Data System (WARDS). Around 84% of all Wisconsin administrative services report into this electronic system. This is a timely, robust data system that has not been used previously to examine drug overdoses and presents an analytical challenge as it contains many free text fields.Methods: Wisconsin’s ambulance data system is robust, well-populated, and includes the majority of Emergency Medical Services (EMS) within the state. The analytic challenge with this data is that most of the reported fields are free text, which can be difficult to analyze on a population level. Wisconsin created a case definition using SAS regular expressions to take advantage of the free text fields. A combination of fields (chief complaint, secondary complaint, medications given, and the EMS narrative) were used to determine if the ambulance run was due to an opioid overdose. It was necessary to create a definition that used a diverse combination of phrases as free text fields are prone to spelling errors and there are many phrases used to identify opioid overdoses. It was also necessary to create a definition for unwanted phrases that signal a false positive, for example, “withdrawal”.Results: Wisconsin’s opioid definition uses regular expressions to search for the words “heroin”, “opioid”, “narcan”, or “methadone” (including various spellings). The overdose definition searches for words and phrases like “drug abuse”, “drug use”, “poisoning”, “drug ingestion”, and “overdose”. The medication administered fields are examined for “narcan”. In Wisconsin, the medication is listed every time it is used, so it is possible to determine the number of times Narcan was administered to a single person as well as how many ambulance runs used at least one dose of Narcan. False positives are identified with words and phrases like “withdrawal”, “detox”, and if Narcan was given but there is no indication that the ambulance run was due to drugs. From January 2016 – June 2017, Wisconsin had over 917,000 ambulance runs for people aged 11 years and older. We excluded non-emergency ambulance runs, like medical transports, and so our final denominator was 627,536 runs (32% of all runs were classified as non-emergencies). Suspected opioid overdoses were determined to be 1% of emergency ambulance runs. Narcan was administered in a total of 5,900 runs and the false positive flag picked up 10,399 runs that may not have been due to suspected opioid overdoses. Applying all of these components together, it was determined that in Wisconsin from January 2016 – June 2017, there were 4,041 emergency ambulance runs due to suspected, unintentional opioid overdoses for people 11 years and older (rate of 6 per 1,000 people).Conclusions: The use of regular expressions enables Wisconsin to extend analyses to data systems that contain robust information within free text fields. Within Wisconsin, this has been utilized to enhance opioid overdose surveillance with the use of a rapid data system previously not examined. Ambulance run information is a valuable resource to Wisconsin with the opioid epidemic. By creating case definitions with free text fields, we can quantify ambulance runs on a population level and create linkable analytic data sets to provide a more complete picture of the health of Wisconsin.

scholarly journals Assessment of a Surveillance Case Definition for Heroin Overdose in Emergency Medical Services Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Michael D. Singleton ◽  
Peter J. Rock
Keyword(s):  
Emergency Medical Services ◽  
Drug Overdose ◽  
Case Definition ◽  
Opioid Overdose ◽  
Data Systems ◽  
Case Definitions ◽  
Heroin Overdose ◽  
Emergency Medical ◽  
Icd 10 ◽  
Data Elements

ObjectiveThe aims of this project were 1) to assess the validity of a surveillance case definition for identifying heroin overdoses (HOD) in a NEMSIS 3–compliant, state ambulance reporting system; and 2) to develop an approach that can be applied to assess the validity of case definitions for other types of drug overdose events in similar data state data systems.IntroductionIn 2016, the Centers for Disease Control and Prevention funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize state Emergency Medical Services (EMS) and emergency department (ED) syndromic surveillance (SyS) data systems to increase timeliness of state data on drug overdoses. A key aspect of the ESOOS program is the development and validation of case definitions for drug overdoses for EMS and ED SyS data systems. Kentucky’s ESOOS team conducted a pilot validation study of a candidate EMS case definition for HOD, using data from the Kentucky State Ambulance Reporting System (KStARS). We examined internal, face validity of the EMS HOD case definition by reviewing pertinent information captured in KStARS data elements; and we examined external agreement with HOD cases identified Kentucky’s statewide hospital billing database.MethodsFrom KStARS, we extracted EMS emergent transports by any ambulance service to hospitals in a single, large health care system in Kentucky. We included responses with dispatch dates between January 1, 2017 and March 31, 2017. From Kentucky’s statewide hospital claims data system, we extracted inpatient discharges, ED visits and observational stays at the destination hospitals, with admit dates in the same range. We classified EMS cases as HOD based on specific combinations of the following criteria for EMS data elements: primary or secondary provider impression of heroin poisoning (T40.1X4), heroin-related keywords in the patient care narrative or chief complaint, and patient’s response to naloxone as indicated in the medications list1. We used standard drug overdose case definitions for ICD-10-CM-coded hospital billing data2 to classify hospital records from the destination facilities to the same categories. We produced descriptive analyses of the heroin overdose cases detected in both data sources, EMS and hospital. To assess the degree of overlap in the HOD cases identified by the two data systems, we matched the identified EMS HOD cases against the entire set of UKHC hospital cases. Finally, we assessed the validity of the classification of EMS cases as heroin overdoses by reviewing the EMS patient care narratives and related EMS data elements, as well as the ICD-10-CM hospital diagnostic codes for cases that matched to a hospital record.ResultsWe identified 5,517 emergent EMS transports to the destination hospitals in the first quarter of 2017. Of these, 94 (17/1,000) were identified by our case definition as a HOD. We identified 29,631 unduplicated, emergent encounters at the destination hospitals (including inpatient discharges, ED visits, and observational stays; and excluding elective and newborn encounters). Of these, 105 (3.5/1,000) included a diagnostic code for HOD. Linkage of EMS and hospital cases indicated that 141 unique HOD cases were identified in the two files combined. Of these, 58 (41%) were identified as HOD in both systems. 23 HOD cases identified in EMS were matched to a hospital record that had no mention of a HOD; and 13 could not be matched to a hospital record. Additionally, 47 HOD cases identified in the destination hospitals were not matched to an EMS transport to those destination facilities. Overall, 76 out of the 94 (81%) EMS cases identified as heroin overdoses were judged likely to be true heroin overdoses, as indicated by either 1) positive response to naloxone and patient admission of recent heroin use, or 2) hospital diagnosis of heroin overdose, or both. For 2% of identified cases, there was evidence of a false positive finding. The remaining 17% of identified heroin cases were inconclusive: there was information suggestive of opioid overdose, but no clear evidence to suggest, nor to rule out, that the opioid was heroin. Generally, inconclusive cases were identified as heroin overdoses due to positive response to naloxone, combined with mention of the word “heroin” in the narrative that did not indicate an HOD. Examples of the latter include negations (patient denies heroin use) or a bystander who stated that the patient had a history of heroin use.ConclusionsWe assessed the performance of a straightforward case definition for heroin overdose for EMS data. Face validity of 81% of identified heroin overdoses was supported by clerical review of EMS records and/or hospital ICD-10-CM diagnostic codes. Some proportion of the other 19% of cases that were identified as heroin overdoses may have been overdoses involving opioids other than heroin, but we could not quantify that proportion based on the available information.Future work will consider sensitivity (true heroin overdoses that may fail to be captured by this case definition) and refinements to the basic definition that may yield improved results. Lessons learned from this pilot project will inform subsequent, larger-scale validation studies for EMS drug overdose case definitions.References1. Rhode Island Enhanced State Opioid Overdose Surveillance (ESOOS). Case Definition for Emergency Medical Services. Aug 2017.2. Injury Surveillance Workgroup 7. Consensus Recommendations for National and State Poisoning Surveillance. The Safe States Alliance. Atlanta, GA. April 2012.

A Mixed-Methods Comparison of a National and State Opioid Overdose Surveillance Definition

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 31S-39S
Author(s):  
Danielle M. Brathwaite ◽  
Catherine S. Wolff ◽  
Amy I. Ising ◽  
Scott K. Proescholdbell ◽  
Anna E. Waller
Keyword(s):  
North Carolina ◽  
False Positive ◽  
False Positive Rate ◽  
International Classification Of Diseases ◽  
Opioid Overdose ◽  
The North ◽  
Classification Of Diseases ◽  
Positive Rate ◽  
Icd 10 ◽  
Ed Visits

Objectives We assessed the differences between the first version of the Centers for Disease Control and Prevention (CDC) opioid surveillance definition for suspected nonfatal opioid overdoses (hereinafter, CDC definition) and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) surveillance definition to determine whether the North Carolina definition should include additional International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and/or chief complaint keywords. Methods Two independent reviewers retrospectively reviewed data on North Carolina emergency department (ED) visits generated by components of the CDC definition not included in the NC DETECT definition from January 1 through July 31, 2018. Clinical reviewers identified false positives as any ED visit in which available evidence supported an alternative explanation for patient presentation deemed more likely than an opioid overdose. After individual assessment, reviewers reconciled disagreements. Results We identified 2296 ED visits under the CDC definition that were not identified under the NC DETECT definition during the study period. False-positive rates ranged from 2.6% to 41.4% for codes and keywords uniquely identifying ≥10 ED visits. Based on uniquely identifying ≥10 ED visits and a false-positive rate ≤10.0%, 4 of 16 ICD-10-CM codes evaluated were identified for NC DETECT definition inclusion. Only 2 of 25 keywords evaluated, “OD” and “overdose,” met inclusion criteria to be considered a meaningful addition to the NC DETECT definition. Practice Implications Quantitative and qualitative trends in coding and keyword use identified in this analysis may prove helpful for future evaluations of surveillance definitions.

scholarly journals Validation of a Case Definition for Pediatric Brain Injury Using Administrative Data

2017 ◽  
Vol 44 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Jane McChesney-Corbeil ◽  
Karen Barlow ◽  
Hude Quan ◽  
Guanmin Chen ◽  
Samuel Wiebe ◽  
...  
Keyword(s):  
Brain Injury ◽  
Administrative Data ◽  
Predictive Value ◽  
External Validation ◽  
Secondary Data ◽  
Population Based ◽  
Case Definition ◽  
Case Definitions ◽  
Icd 10 ◽  
Pediatric Tbi

AbstractBackground: Health administrative data are a common population-based data source for traumatic brain injury (TBI) surveillance and research; however, before using these data for surveillance, it is important to develop a validated case definition. The objective of this study was to identify the optimal International Classification of Disease , edition 10 (ICD-10), case definition to ascertain children with TBI in emergency room (ER) or hospital administrative data. We tested multiple case definitions. Methods: Children who visited the ER were identified from the Regional Emergency Department Information System at Alberta Children’s Hospital. Secondary data were collected for children with trauma, musculoskeletal, or central nervous system complaints who visited the ER between October 5, 2005, and June 6, 2007. TBI status was determined based on chart review. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each case definition. Results: Of 6639 patients, 1343 had a TBI. The best case definition was, “1 hospital or 1 ER encounter coded with an ICD-10 code for TBI in 1 year” (sensitivity 69.8% [95% confidence interval (CI), 67.3-72.2], specificity 96.7% [95% CI, 96.2-97.2], PPV 84.2% [95% CI 82.0-86.3], NPV 92.7% [95% CI, 92.0-93.3]). The nonspecific code S09.9 identified >80% of TBI cases in our study. Conclusions: The optimal ICD-10–based case definition for pediatric TBI in this study is valid and should be considered for future pediatric TBI surveillance studies. However, external validation is recommended before use in other jurisdictions, particularly because it is plausible that a larger proportion of patients in our cohort had milder injuries.

scholarly journals A Recommender for Choosing Data Systems based on Application Profiling and Benchmarking

2021 ◽  
Author(s):  
Elton Figueiredo de Souza Soares ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Marcelo de Oliveira Costa Machado ◽  
Leonardo Guerreiro Azevedo
Keyword(s):  
Empirical Evidence ◽  
Informed Decision ◽  
Data System ◽  
Data Driven ◽  
Data Systems ◽  
Ongoing Work ◽  
Wide Range

In our data-driven society, there are hundreds of possible data systems in the market with a wide range of configuration parameters, making it very hard for enterprises and users to choose the most suitable data systems. There is a lack of representative empirical evidence to help users make an informed decision. Using benchmark results is a widely adopted practice, but like there are several data systems, there are various benchmarks. This ongoing work presents an architecture and methods of a system that supports the recommendation of the most suitable data system for an application. We also illustrates how the recommendation would work in a fictitious scenario.

scholarly journals Secondary transmissions during the outbreak of Shiga toxin-producing Escherichia coli O104 in Hesse, Germany, 2011

2011 ◽  
Vol 16 (31) ◽  
Author(s):  
A M Hauri ◽  
U Götsch ◽  
I Strotmann ◽  
J Krahn ◽  
G Bettge-Weller ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Shiga Toxin ◽  
Case Definition ◽  
Case Definitions ◽  
Secondary Transmission ◽  
E Coli ◽  
Outbreak Area ◽  
The Family ◽  
Satellite Clusters ◽  
Outbreak Case

During the recent outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany most cases notified in the State of Hesse (6 million inhabitants) were linked to satellite clusters or had travelled to the outbreak area in northern Germany. Intensified surveillance was introduced to rapidly identify cases not linked to known clusters or cases and thus to obtain timely information on possible further contaminated vehicles distributed in Hesse, as well to describe the risk of secondary transmission among known cases. As of 2 August 2011*, 56 cases of haemolytic uraemic syndrome (HUS) including two fatal cases, and 124 cases of STEC gastroenteritis meeting the national case definitions have been reported in Hesse. Among the 55 HUS and 81 STEC gastroenteritis cases that met the outbreak case definition, one HUS case and eight STEC gastroenteritis cases may have acquired their infection through secondary transmission. They include six possible transmissions within the family, two possible nosocomial and one possible laboratory transmission. Our results do not suggest an increased transmissibility of the outbreak strain compared to what is already known about E. coli O157 and other STEC serotypes.

scholarly journals Retrospective screening of routine respiratory samples revealed undetected community transmission and missed intervention opportunities for SARS-CoV-2 in the United Kingdom

2021 ◽  
Vol 102 (6) ◽  
Author(s):  
Joseph G. Chappell ◽  
Theocharis Tsoleridis ◽  
Gemma Clark ◽  
Louise Berry ◽  
Nadine Holmes ◽  
...  
Keyword(s):  
Sequence Data ◽  
Infected Individual ◽  
Case Definition ◽  
Case Definitions ◽  
Risk Area ◽  
High Risk Area ◽  
Epidemiological Risk ◽  
Distinct Lineage ◽  
The Uk ◽  
Archival Specimens

In the early phases of the SARS coronavirus type 2 (SARS-CoV-2) pandemic, testing focused on individuals fitting a strict case definition involving a limited set of symptoms together with an identified epidemiological risk, such as contact with an infected individual or travel to a high-risk area. To assess whether this impaired our ability to detect and control early introductions of the virus into the UK, we PCR-tested archival specimens collected on admission to a large UK teaching hospital who retrospectively were identified as having a clinical presentation compatible with COVID-19. In addition, we screened available archival specimens submitted for respiratory virus diagnosis, and dating back to early January 2020, for the presence of SARS-CoV-2 RNA. Our data provides evidence for widespread community circulation of SARS-CoV-2 in early February 2020 and into March that was undetected at the time due to restrictive case definitions informing testing policy. Genome sequence data showed that many of these early cases were infected with a distinct lineage of the virus. Sequences obtained from the first officially recorded case in Nottinghamshire - a traveller returning from Daegu, South Korea – also clustered with these early UK sequences suggesting acquisition of the virus occurred in the UK and not Daegu. Analysis of a larger sample of sequences obtained in the Nottinghamshire area revealed multiple viral introductions, mainly in late February and through March. These data highlight the importance of timely and extensive community testing to prevent future widespread transmission of the virus.

scholarly journals EP.FRI.745 Information Sustainability: SCR+: A bespoke module to optimise information flows around the Somerset Cancer Register to enhance the multidisciplinary cancer team process

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Fatima Rahman ◽  
Alan Hales ◽  
Ryan Beegan ◽  
David Cable ◽  
David Rew
Keyword(s):  
Reporting System ◽  
Digital Health ◽  
Data System ◽  
Data Systems ◽  
Core System ◽  
Software Application ◽  
Cancer Data ◽  
Cancer Register ◽  
Agile Programming ◽  
National Reporting System

Abstract Background Many surgeons work within multidisciplinary cancer teams. The Somerset Cancer Register (SCR) is a national reporting system for service performance which is in use in more than 100 NHS Trusts. However, the core system has not yet been optimised for MDT users or for the surfacing of clinical data for research and other uses. Methods SCR replaced our legacy cancer reporting system in 2014. Working with the SCR developers, we integrated our cellular pathology and imaging records with the SCR MDT outputs. We subsequently developed SCR+ to optimise workflows for MDT coordinators and information presentation to clinical users.    Results Our HTML-enabled SCR+ software application displays all cancer patients by pathological type and year of presentation on dynamic histograms, for ease of visualisation and interaction. Every selected case is displayed in list order for each and every MDT meeting, with a fast hyperlink to our integral Lifelines EPR interface, to electronic pathology records back to 1990, and to our Breast Cancer Data System for relevant patients. Conclusions The SCR+ module transforms the access and visualisation of cancer workload across our Trust for all authorised MDT users, with appropriate data security. The agile programming methodology allowed us to build a sustainable cancer data system with further development potential. The product substantially enhances user experience, data recall and productivity over legacy systems. Close cooperation between clinically proficient  IT teams and clinicians as the end consumers of digital health data systems yields significant operational benefits at pace and with very modest costs.  

Characterizing Opioid Overdoses Using Emergency Medical Services Data

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 62S-71S
Author(s):  
Josie J. Sivaraman ◽  
Scott K. Proescholdbell ◽  
David Ezzell ◽  
Meghan E. Shanahan
Keyword(s):  
Machine Learning ◽  
North Carolina ◽  
Emergency Medical Services ◽  
Expert Knowledge ◽  
Case Definition ◽  
Medical Services ◽  
Opioid Overdose ◽  
Machine Learning Classification ◽  
Emergency Medical ◽  
Definition Of

Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.

Linking data systems into a collaborative pipeline for geochemical data from field to archive

2021 ◽  
Author(s):  
Kerstin Lehnert ◽  
Daven Quinn ◽  
Basil Tikoff ◽  
Douglas Walker ◽  
Sarah Ramdeen ◽  
...  
Keyword(s):  
Analytical Data ◽  
Data System ◽  
Standard Reference Materials ◽  
Data Management System ◽  
Geochemical Data ◽  
Data Repository ◽  
Data Systems ◽  
Raw Data ◽  
Wide Range ◽  
Data Lifecycle

<div> <p>Management of geochemical data needs to consider the sequence of phases in the lifecycle of these data from field to lab to publication to archive. It also needs to address the large variety of chemical properties measured; the wide range of materials that are analyzed; the different ways, in which these materials may be prepared for analysis; the diversity of analytical techniques and instrumentation used to obtain analytical results; and the many ways used to calibrate and correct raw data, normalize them to standard reference materials, and otherwise treat them to obtain meaningful and comparable results. In order to extract knowledge from the data, they are then integrated and compared with other measurements, formatted for visualization, statistical analysis, or model generation, and finally cleaned and organized for publication and deposition in a data repository. Each phase in the geochemical data lifecycle has its specific workflows and metadata that need to be recorded to fully document the provenance of the data so that others can reproduce the results.</p> </div><div> <p>An increasing number of software tools are developed to support the different phases of the geochemical data lifecycle. These include electronic field notebooks, digital lab books, and Jupyter notebooks for data analysis, as well as data submission forms and templates. These tools are mostly disconnected and often require manual transcription or copying and pasting of data and metadata from one tool to the other. In an ideal world, these tools would be connected so that field observations gathered in a digital field notebook, such as sample locations and sampling dates, can be seamlessly send to an IGSN Allocating Agent to obtain a unique sample identifier with a QR code with a single click. The sample metadata would be readily accessible for the lab data management system that allows the researchers to capture information about the sample preparation, and that connects to the instrumentation to capture instrument settings and the raw data. The data would then be seamlessly accessed by data reduction software, visualized, and further compared to data from global databases that can be directly accessed. Ultimately, a few clicks will allow the user to format the data for publication and archiving.</p> </div><div> <p>Several data systems that support different stages in the lifecycle of samples and sample-based geochemical data have now come together to explore the development of standardized interfaces and APIs and consistent data and metadata schemas to link their systems into an efficient pipeline for geochemical data from the field to the archive. These systems include StraboSpot (www.strabospot.org; data system for digital collection, storage, and sharing of both field and lab data), SESAR (<span>www.geosamples.org</span>; sample registry and allocating agent for IGSN), EarthChem (www.earthchem.org; publishers and repository for geochemical data), Sparrow (sparrow-data.org; data system to organize analytical data and track project- and sample-level metadata), IsoBank (isobank.org; repository for stable isotope data), and MacroStrat (macrostrat.org; collaborative platform for geological data exploration and integration).</p> </div>

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