Natural language processor as a tool to assess heparin induced thrombocytopenia awareness.

The life-threatening consequences of heparin induced thrombocytopenia (HIT) may be prevented with early recognition, prompt heparin withdrawal and direct thrombin inhibitor use. To determine the level of HIT awareness, electronic term recognition software can be used to query the electronic medical record (EMR) to assess the thought process and test ordering behavior of health care providers confronted with falling platelet counts. We sought to assess the awareness of HIT in a large teaching institution using these tools. Mayo Clinic databases were queried to identify a cohort of hospitalized adults receiving heparin (06/1/08-06/1/09). Serial platelet counts for each patient were scrutinized for a 50% decrement from baseline. "Clinician awareness" was defined by mention of HIT (determined by electronic term recognition software) within the hospital record by any member of the healthcare team or requisition of platelet factor 4/heparin antibody testing. During this time period, 34,694 adults were hospitalized and 24,956 received heparin. Only 3,239 (13%) patients had more than 1 platelet count during the hospital stay. Of 199 patients (6.1%) with ≥50% platelet count drop, clinician awareness was 36%. The absolute platelet count was the only independent variable associated with HIT awareness (P < 0.001). Both appropriate platelet count monitoring and HIT awareness are low at this large teaching institution. Software tools for monitoring awareness and providing realtime alerts of significant platelet count decrements may be useful.

Relative impact of risk factors for deep vein thrombosis and pulmonary embolism: a population-based study.

OBJECTIVE: To assess the potential impact of controlling risk factors on the incidence of venous thromboembolism by estimating the population attributable risk (defined as the percentage of all cases of a disease in a population that can be "attributed" to a risk factor) for deep vein thrombosis and pulmonary embolism associated with venous thromboembolism risk factors. METHODS: Using data from a population-based, nested, case-control study of the 625 Olmsted County, Minnesota, residents with a definite first lifetime deep vein thrombosis or pulmonary embolism diagnosed during the 15-year period 1976 to 1990 and 625 unaffected Olmsted County residents matched for age and sex, we developed a conditional logistic regression model appropriate to the matched case-control study design and then estimated attributable risk for the risk factors individually and collectively. RESULTS: Fifty-nine percent of the cases of venous thromboembolism in the community could be attributed to institutionalization (current or recent hospitalization or nursing home residence). Hospitalization for surgery (24%) and for medical illness (22%) accounted for a similar proportion of the cases, while nursing home residence accounted for 13%. The individual attributable risk estimates for malignant neoplasm, trauma, congestive heart failure, central venous catheter or pacemaker placement, neurological disease with extremity paresis, and superficial vein thrombosis were 18%, 12%, 10%, 9%, 7%, and 5%, respectively. Together, the 8 risk factors accounted for 74% of disease occurrence. CONCLUSIONS: Factors associated with institutionalization independently account for more than 50% of all cases of venous thromboembolism in the community. Greater emphasis should be placed on prophylaxis for hospitalized medical patients. Other recognized risk factors account for about 25% of all cases of venous thromboembolism, while the remaining 25% of cases are idiopathic.

Speech recognition as a transcription aid: a randomized comparison with standard transcription.

OBJECTIVE: Speech recognition promises to reduce information entry costs for clinical information systems. It is most likely to be accepted across an organization if physicians can dictate without concerning themselves with real-time recognition and editing; assistants can then edit and process the computer-generated document. Our objective was to evaluate the use of speech-recognition technology in a randomized controlled trial using our institutional infrastructure. DESIGN: Clinical note dictation from physicians in two specialty divisions was randomized to either a standard transcription process or a speech-recognition process. Secretaries and transcriptionists also were assigned randomly to each of these processes. MEASUREMENTS: The duration of each dictation was measured. The amount of time spent processing a dictation to yield a finished document also was measured. Secretarial and transcriptionist productivity, defined as hours of secretary work per minute of dictation processed, was determined for speech recognition and standard transcription. RESULTS: Secretaries in the endocrinology division were 87.3% (confidence interval, 83.3%, 92.3%) as productive with the speech-recognition technology as implemented in this study as they were using standard transcription. Psychiatry transcriptionists and secretaries were similarly less productive. Author, secretary, and type of clinical note were significant (p < 0.05) predictors of productivity. CONCLUSION: When implemented in an organization with an existing document-processing infrastructure (which included training and interfaces of the speech-recognition editor with the existing document entry application), speech recognition did not improve the productivity of secretaries or transcriptionists.

The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.

Mayo Clinic's Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic's analytic and decision-making processes. It is an interconnected piece of Mayo Clinic's Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions.