Negotiating agency: Amish and ultra-Orthodox women’s responses to the Internet

This study explores how women in two devout religious communities cope with the Internet and its apparent incompatibility with their communities’ values and practices. Questionnaires containing both closed and open-ended questions were completed by 82 participants, approximately half from each community. While their discourses included similar framings of danger and threat, the two groups manifested different patterns of Internet use (and nonuse). Rigorous adherence to religious dictates is greatly admired in these communities, and the women take pride in manipulating their status in them. Their agency is reflected in how they negotiate the tension inherent in their roles as both gatekeepers and agents-of-change, which are analyzed as valuable currencies in their cultural and religious markets

Nursing considerations to complement the Surviving Sepsis Campaign guidelines

Objectives: To provide a series of recommendations based on the best available evidence to guide clinicians providing nursing care to patients with severe sepsis. Design: Modified Delphi method involving international experts and key individuals in subgroup work and electronic-based discussion among the entire group to achieve consensus. Methods: We used the Surviving Sepsis Campaign guidelines as a framework to inform the structure and content of these guidelines. We used the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system to rate the quality of evidence from high (A) to very low (D) and to determine the strength of recommendations, with grade 1 indicating clear benefit in the septic population and grade 2 indicating less confidence in the benefits in the septic population. In areas without complete agreement between all authors, a process of electronic discussion of all evidence was undertaken until consensus was reached. This process was conducted independently of any funding. Results: Sixty-three recommendations relating to the nursing care of severe sepsis patients are made. Prevention recommendations relate to education, accountability, surveillance of nosocomial infections, hand hygiene, and prevention of respiratory, central line-related, surgical site, and urinary tract infections, whereas infection management recommendations related to both control of the infection source and transmission-based precautions. Recommendations related to initial resuscitation include improved recognition of the deteriorating patient, diagnosis of severe sepsis, seeking further assistance, and initiating early resuscitation measures. Important elements of hemodynamic support relate to improving both tissue oxygenation and macrocirculation. Recommendations related to supportive nursing care incorporate aspects of nutrition, mouth and eye care, and pressure ulcer prevention and management. Pediatric recommendations relate to the use of antibiotics, steroids, vasopressors and inotropes, fluid resuscitation, sedation and analgesia, and the role of therapeutic end points. Conclusion: Consensus was reached regarding many aspects of nursing care of the severe sepsis patient. Despite this, there is an urgent need for further evidence to better inform this area of critical care

Coding of procedures documented by general practitioners in Swedish primary care-an explorative study using two procedure coding systems

<p>Abstract</p> <p>Background</p> <p>Procedures documented by general practitioners in primary care have not been studied in relation to procedure coding systems. We aimed to describe procedures documented by Swedish general practitioners in electronic patient records and to compare them to the Swedish Classification of Health Interventions (KVÅ) and SNOMED CT.</p> <p>Methods</p> <p>Procedures in 200 record entries were identified, coded, assessed in relation to two procedure coding systems and analysed.</p> <p>Results</p> <p>417 procedures found in the 200 electronic patient record entries were coded with 36 different Classification of Health Interventions categories and 148 different SNOMED CT concepts. 22.8% of the procedures could not be coded with any Classification of Health Interventions category and 4.3% could not be coded with any SNOMED CT concept. 206 procedure-concept/category pairs were assessed as a complete match in SNOMED CT compared to 10 in the Classification of Health Interventions.</p> <p>Conclusions</p> <p>Procedures documented by general practitioners were present in nearly all electronic patient record entries. Almost all procedures could be coded using SNOMED CT.</p> <p>Classification of Health Interventions covered the procedures to a lesser extent and with a much lower degree of concordance. SNOMED CT is a more flexible terminology system that can be used for different purposes for procedure coding in primary care.</p

Do coder characteristics influence validity of ICD-10 hospital discharge data?

<p>Abstract</p> <p>Background</p> <p>Administrative data are widely used to study health systems and make important health policy decisions. Yet little is known about the influence of coder characteristics on administrative data validity in these studies. Our goal was to describe the relationship between several measures of validity in coded hospital discharge data and 1) coders' volume of coding (≥13,000 vs. <13,000 records), 2) coders' employment status (full- vs. part-time), and 3) hospital type.</p> <p>Methods</p> <p>This descriptive study examined 6 indicators of face validity in ICD-10 coded discharge records from 4 hospitals in Calgary, Canada between April 2002 and March 2007. Specifically, mean number of coded diagnoses, procedures, complications, Z-codes, and codes ending in 8 or 9 were compared by coding volume and employment status, as well as hospital type. The mean number of diagnoses was also compared across coder characteristics for 6 major conditions of varying complexity. Next, kappa statistics were computed to assess agreement between discharge data and linked chart data reabstracted by nursing chart reviewers. Kappas were compared across coder characteristics.</p> <p>Results</p> <p>422,618 discharge records were coded by 59 coders during the study period. The mean number of diagnoses per record decreased from 5.2 in 2002/2003 to 3.9 in 2006/2007, while the number of records coded annually increased from 69,613 to 102,842. Coders at the tertiary hospital coded the most diagnoses (5.0 compared with 3.9 and 3.8 at other sites). There was no variation by coder or site characteristics for any other face validity indicator. The mean number of diagnoses increased from 1.5 to 7.9 with increasing complexity of the major diagnosis, but did not vary with coder characteristics. Agreement (kappa) between coded data and chart review did not show any consistent pattern with respect to coder characteristics.</p> <p>Conclusions</p> <p>This large study suggests that coder characteristics do not influence the validity of hospital discharge data. Other jurisdictions might benefit from implementing similar employment programs to ours, e.g.: a requirement for a 2-year college training program, a single management structure across sites, and rotation of coders between sites. Limitations include few coder characteristics available for study due to privacy concerns.</p

Predictive modeling of structured electronic health records for adverse drug event detection

BACKGROUND: The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. METHODS: Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. RESULTS: Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. CONCLUSIONS: We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two