Use of data mining tools in medicat...

Use of data mining tools in medication error analysis

Topics in health information management November 2002

Data mining and machine learning are artificial intelligence technologies that analyze data from different perspectives and summarize it into useful information. The objective of this meditation was to determine if data mining tools are useful in identifying underlying factors related to medication errors. (1) Incident reports (n = 2700) from a large urban hospital during a five-year period were analyzed. Errors were classified in relation to the one responsible (ie, nurse, physician, pharmacist) and location where the error occurr (ie, general care, surgery intensive care, difficulty department). Outcome variables assessed were near misses and adverse remedy events (ADE) associated with medication errors. Data mining and machine learning technologies were used to analyze these variables.

Findings. Data showed that 807% of the medication errors reached patients, and 193% were classified as near misses (ie, the error was caught before the medication was given to the patient). Data mining analysis accurately classified 72% of the medication error cases and revealed that the three greatest in number important factors in predicting an ADE were pattern of medication, proximal cause (ie, why) and place where the error occurr The body responsible, proximal cause, and place were the factors associated with near misses. Additionally, analysis revealed that if management was not followed, errors reached patients 93% of the time if a feed at the breast was the responsible person, 68% of the time if a pharmacist was the responsible part and 75% of the time if a physician was the responsible somebody Conversely, if a nurse was responsible and the medication error was the issue of miscommunication, an ADE was likely to eventuate 20% of the tune, compared to 55% of the time if a pharmacist was responsible for the medication error.

Clinical implications. This research revealed that data mining tools can identify patterns and relationships that are not easily identified using conventional data analysis. In searching for ways to improve patient safety, perioperative managers must consider the use of techniques like as data mining to identify hidden patterns and underlying factors related to medication errors and other patient safety issues in like manner corrective actions can be evolveed and implemented effectively.

Hair removal and surgical site infection rates

Otolaryngology--head and neck surgery January 2003

Removal of hair from a surgical site is an identified risk factor for surgical site infection (SSI), and the risk increases when hair is remov on shaving. Despite this fact, hair removal according to shaving is a common practice before surgery onward the skull. This retrospective contemplation was undertaken to evaluate SSI rates after cranium base resections for which hair was not remov (2) The SSI rate in 175 patients who underwent brain base resections without hair removal was assessed retrospectively and compared to published SSI rates in patients who had undergone similar acts for which hair was removed

Findings. The overall SSI rate in the assemblage that did not have hair remov was 11% (ie, sum of two units SSIs in 175 procedures). This infection rate was equivalent to published SSI rates for similar operations for which hair was removed

Clinical implications. The finding of no statistically significant difference in SSI rates between conducts for which hair was remov and manner of proceedings for which hair was not remov before cranium base surgery is clinically relevant. These be deriveds strongly suggest that skull base surgical measures without hair removal do not carry additional risk for SSI, and they potentially could have a positive impact in succession the psychosocial well-being of the patient. Perioperative nurtures can urge surgeons and other clinicians to reevaluate commonly practiced processs (ie, sacred cows) including removal of hair before surgical practices on the skull.

Sleev cytobrush versus endocervical curette sampling

Obstetrics & gynecology March 2003

Although false negative and false positive inferences plague the endocervical curette standard it continues to be used by dint of many health care providers in the evaluation of women with abnormal cervical cytology. This randomized inquiry was undertaken to compare endocervical specimens obtained using an endocervical curette to those obtained using a sleev cytobrush. (3) the pair endocervical curette and cytobrush sampling were performed forward 62 patients undergoing cervical conization or hysterectomy.

The sampling order was randomized. In the same group, cytobrush sampling was performed first, and then an endocervical curette sampling was performed before surgery In the other collection an endocervical curette sampling was performed first, and then a cytobrush sampling was performed before surgery Specimens were assessed for adequacy for diagnosis, the appearance or absence of neoplasm, and the order of neoplasm if present. Descriptive statistics were used to analyze differences between the pair groups.