Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

<p>Abstract</p> <p>Background</p> <p>Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.</p> <p>Methods</p> <p>Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.</p> <p>Results</p> <p>NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.</p> <p>Conclusions</p> <p>We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.</p

PhD

dissertationSolid organ transplant has become an established and cost-effective therapy for persons with end-stage renal, hepatic, or pancreatic disease. Long-term survival depends on the patient, the surgical procedure, the donated organ, the management of immunosuppressive therapy, and the detection and management of complications following surgery. Clinical information concerning transplant patients is voluminous and difficult to manage when using paper records. A system analysis was performed to assess the information system needs of the liver, kidney, and pancreas transplant program at LDS Hospital in Salt Lake City, Utah. After evaluating workflow, data collection forms, decision support needs, and functional requirements, we designed and implemented an extendable information system to support the process of care following liver transplantation. Tools for collecting and entering information into the electronic health record (EHR) were developed, including an operative note and data entry forms for external laboratory results and transplant-related information. A new information model, consistent with national interoperability standards, was developed for storing donor-related information in a transplant patient's record. Once coded, external laboratory information was available in the EHR, clinicians could view both external and Intermountain Health Care (IHC) laboratory results in chronological order. This view was particularly usefiil when the paper flowchart was not available. The laboratory and transplant-related information triggered decision support alerts that were designed to notify nurses when liver transplant patients had new, abnormal, or overdue laboratory results. The alerts improved the quality of laboratory information used for outpatient care. Compared with the traditional process of reporting laboratory results with faxes and printouts, the alerts resulted in more timely, complete, and efficient reporting of laboratory results. The time for responding to laboratory results was cut by one-third from a median of 33 to 9 hours after specimen collection. Results were reported in realtime"" and clinicians could act on information the same-day results arrived. The components developed for this project addressed only part of the needs of the liver, kidney, and pancreas transplant program; however, the system design can be enhanced in the fixture to meet other program needs."

Identification of pneumonia and influenza deaths using the death certificate pipeline

Abstract Background Death records are a rich source of data, which can be used to assist with public surveillance and/or decision support. However, to use this type of data for such purposes it has to be transformed into a coded format to make it computable. Because the cause of death in the certificates is reported as free text, encoding the data is currently the single largest barrier of using death certificates for surveillance. Therefore, the purpose of this study was to demonstrate the feasibility of using a pipeline, composed of a detection rule and a natural language processor, for the real time encoding of death certificates using the identification of pneumonia and influenza cases as an example and demonstrating that its accuracy is comparable to existing methods. Results A Death Certificates Pipeline (DCP) was developed to automatically code death certificates and identify pneumonia and influenza cases. The pipeline used MetaMap to code death certificates from the Utah Department of Health for the year 2008. The output of MetaMap was then accessed by detection rules which flagged pneumonia and influenza cases based on the Centers of Disease and Control and Prevention (CDC) case definition. The output from the DCP was compared with the current method used by the CDC and with a keyword search. Recall, precision, positive predictive value and F-measure with respect to the CDC method were calculated for the two other methods considered here. The two different techniques compared here with the CDC method showed the following recall/ precision results: DCP: 0.998/0.98 and keyword searching: 0.96/0.96. The F-measure were 0.99 and 0.96 respectively (DCP and keyword searching). Both the keyword and the DCP can run in interactive form with modest computer resources, but DCP showed superior performance. Conclusion The pipeline proposed here for coding death certificates and the detection of cases is feasible and can be extended to other conditions. This method provides an alternative that allows for coding free-text death certificates in real time that may increase its utilization not only in the public health domain but also for biomedical researchers and developers. Trial Registration This study did not involved any clinical trials.</p

Evaluation of knowledge resources for public health reporting logic: Implications for knowledge authoring and management

To control disease, laboratories and providers are required to report conditions to public health authorities. Reporting logic is defined in a variety of resources, but there is no single resource available for reporters to access the list of reportable events and computable reporting logic for any jurisdiction. In order to develop evidence-based requirements for authoring such knowledge, we evaluated reporting logic in the Council of State and Territorial Epidemiologist (CSTE) position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface; we evaluated codes in the Reportable Condition Mapping Tables (RCMT) relative to the nationally-defined reporting logic, and described the high level business processes and knowledge required to support laboratory-based public health reporting. We focused on logic for viral hepatitis. We found that CSTE tabular logic was unnecessarily complex (sufficient conditions superseded necessary and optional con¬ditions) and was sometimes true for more than one reportable event: we uncovered major overlap in the logic between acute and chronic hepatitis B (52%), acute and Past and Present hepatitis C (90%). We found that the RCMT includes codes for all hepatitis criteria, but includes addition codes for tests not included in the criteria. The proportion of hepatitis variant-related codes included in RCMT that correspond to a criterion in the hepatitis-related position statements varied between hepatitis A (36%), acute hepatitis B (16%), chronic hepatitis B (64%), acute hepatitis C (96%), and past and present hepatitis C (96%). Public health epidemiologists have the need to communicate parameters other than just the name of a disease or organism that should be reported, such as the status and specimen sources. Existing knowledge resources should be integrated, harmonized and made computable. Our findings identified functionality that should be provided by future knowledge management systems to support epidemiologists as they communicate reporting rules for their jurisdiction

Building an Ontology for Identity Resolution in Healthcare and Public Health

Integration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. Such integration, however, depends on correctly matching patient-specific records using demographic identifiers.  Without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity.Objectives: Our objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology’s ability to model identity-changing events over time.Methods: We interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. We searched for existing relevant ontologies. We validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models.Results:  We chose the Simple Event Model (SEM) to describe events in early childhood and integrated the Clinical Element Model (CEM) for demographic information.  We demonstrated the ability of the combined SEM-CEM ontology to model identity events over time.Conclusion: The use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage

Building an Ontology for Identity Resolution in Healthcare and Public Health

Integration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. Such integration, however, depends on correctly matching patient-specific records using demographic identifiers. Without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity. Objectives: Our objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology’s ability to model identity-changing events over time. Methods: We interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. We searched for existing relevant ontologies. We validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models. Results: We chose the Simple Event Model (SEM) to describe events in early childhood and integrated the Clinical Element Model (CEM) for demographic information. We demonstrated the ability of the combined SEM-CEM ontology to model identity events over time. Conclusion: The use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage

Evaluation of knowledge resources for public health reporting logic: Implications for knowledge authoring and management

To control disease, laboratories and providers are required to report conditions to public health authorities. Reporting logic is defined in a variety of resources, but there is no single resource available for reporters to access the list of reportable events and computable reporting logic for any jurisdiction. In order to develop evidence-based requirements for authoring such knowledge, we evaluated reporting logic in the Council of State and Territorial Epidemiologist (CSTE) position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface; we evaluated codes in the Reportable Condition Mapping Tables (RCMT) relative to the nationally-defined reporting logic, and described the high level business processes and knowledge required to support laboratory-based public health reporting. We focused on logic for viral hepatitis. We found that CSTE tabular logic was unnecessarily complex (sufficient conditions superseded necessary and optional conditions) and was sometimes true for more than one reportable event: we uncovered major overlap in the logic between acute and chronic hepatitis B (52%), acute and Past and Present hepatitis C (90%). We found that the RCMT includes codes for all hepatitis criteria, but includes addition codes for tests not included in the criteria. The proportion of hepatitis variant-related codes included in RCMT that correspond to a criterion in the hepatitis-related position statements varied between hepatitis A (36%), acute hepatitis B (16%), chronic hepatitis B (64%), acute hepatitis C (96%), and past and present hepatitis C (96%). Public health epidemiologists have the need to communicate parameters other than just the name of a disease or organism that should be reported, such as the status and specimen sources. Existing knowledge resources should be integrated, harmonized and made computable. Our findings identified functionality that should be provided by future knowledge management systems to support epidemiologists as they communicate reporting rules for their jurisdiction

Development of an Information Model for Storing Organ Donor Data Within an Electronic Medical Record

Objective: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests. Design: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(∧)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the “source” of the data (donor) and the “name” of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR. Results: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the “subject” of the test. Conclusion: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR