Identifying risk factors for healthcare-associated infections from electronic medical record home address data

<p>Abstract</p> <p>Background</p> <p>Residential address is a common element in patient electronic medical records. Guidelines from the U.S. Centers for Disease Control and Prevention specify that residence in a nursing home, skilled nursing facility, or hospice within a year prior to a positive culture date is among the criteria for differentiating healthcare-acquired from community-acquired methicillin-resistant <it>Staphylococcus aureus </it>(MRSA) infections. Residential addresses may be useful for identifying patients residing in healthcare-associated settings, but methods for categorizing residence type based on electronic medical records have not been widely documented. The aim of this study was to develop a process to assist in differentiating healthcare-associated from community-associated MRSA infections by analyzing patient addresses to determine if residence reported at the time of positive culture was associated with a healthcare facility or other institutional location.</p> <p>Results</p> <p>We identified 1,232 of the patients (8.24% of the sample) with positive cultures as probable cases of healthcare-associated MRSA based on residential addresses contained in electronic medical records. Combining manual review with linking to institutional address databases improved geocoding rates from 11,870 records (79.37%) to 12,549 records (83.91%). Standardization of patient home address through geocoding increased the number of matches to institutional facilities from 545 (3.64%) to 1,379 (9.22%).</p> <p>Conclusions</p> <p>Linking patient home address data from electronic medical records to institutional residential databases provides useful information for epidemiologic researchers, infection control practitioners, and clinicians. This information, coupled with other clinical and laboratory data, can be used to inform differentiation of healthcare-acquired from community-acquired infections. The process presented should be extensible with little or no added data costs.</p

Matching records in multiple databases using a hybridization of several technologies.

A major problem with integrating information from multiple databases is that the same data objects can exist in inconsistent data formats across databases and a variety of attribute variations, making it difficult to identify matching objects using exact string matching. In this research, a variety of models and methods have been developed and tested to alleviate this problem. A major motivation for this research is that the lack of efficient tools for patient record matching still exists for health care providers. This research is focused on the approximate matching of patient records with third party payer databases. This is a major need for all medical treatment facilities and hospitals that try to match patient treatment records with records of insurance companies, Medicare, Medicaid and the veteran\u27s administration. Therefore, the main objectives of this research effort are to provide an approximate matching framework that can draw upon multiple input service databases, construct an identity, and match to third party payers with the highest possible accuracy in object identification and minimal user interactions. This research describes the object identification system framework that has been developed from a hybridization of several technologies, which compares the object\u27s shared attributes in order to identify matching object. Methodologies and techniques from other fields, such as information retrieval, text correction, and data mining, are integrated to develop a framework to address the patient record matching problem. This research defines the quality of a match in multiple databases by using quality metrics, such as Precision, Recall, and F-measure etc, which are commonly used in Information Retrieval. The performance of resulting decision models are evaluated through extensive experiments and found to perform very well. The matching quality performance metrics, such as precision, recall, F-measure, and accuracy, are over 99%, ROC index are over 99.50% and mismatching rates are less than 0.18% for each model generated based on different data sets. This research also includes a discussion of the problems in patient records matching; an overview of relevant literature for the record matching problem and extensive experimental evaluation of the methodologies, such as string similarity functions and machine learning that are utilized. Finally, potential improvements and extensions to this work are also presented

A descriptive study of the burden of animal-related trauma at Cork University Hospital

Farming is the most dangerous occupation in Ireland1 and the incidence of farm accidents is rising. This study examines major farm animal-related trauma treated at Cork University Hospital over a 5 year period. There were 54 patients admitted to Cork University Hospital (C.U.H.) with major farm animal-related trauma. The median age was 56 years, 85% were male and the median hospital length of stay was four days. Older patients had longer lengths of stay; 5.5 vs 4 days (p=0.026). Tibia/fibula fractures were the most common injuries (N=13, 24%); head injury occurred in six patients (11%). There were 32 (59%) patients who required surgery, the majority for orthopaedic injuries. There were nine patients (16.7%) admitted to the intensive care unit; their median ICU stay was four days. Injury prevention and treatment strategies require that the age profile, mechanism of injury and injury patterns of farmers sustaining animal-related trauma is recognised

Cooperation across multiple healthcare clinics on the cloud

Many healthcare units are creating cloud strategies and mi- gration plans in order to exploit the benefits of cloud based computing. This generally involves collaboration between healthcare specialists and data management researchers to create a new wave of healthcare tech- nology and services. However, in many cases the technology pioneers are ahead of government policies as cloud based storage of healthcare data is not yet permissible in many jurisdictions. One approach is to store anonymised data on the cloud and maintain all identifying data locally. At login time, a simple protocol can be developed to allow clinicians to combine both sets of data for selected patients for the current session. However, the management of o↵-cloud identifying data requires a frame- work to ensure sharing and availability of data within clinics and the ability to share data between users in remote clinics. In this paper, we introduce the PACE healthcare architecture which uses a combination of Cloud and Peer-to-Peer technologies to model healthcare units or clin- ics where o↵-cloud data is accessible to all, and where exchange of data between remote healthcare units is also facilitated