A Patient Activities of Daily Living Scale for Amyotrophic Lateral Sclerosis

Background: Motor neuron disorders are rare, progressive neurodegenerative diseases which affect multiple domains of motor function. The ability to assess function from home using an electronic medical record (EMR) would facilitate pragmatic studies. Objective: To develop a Patient Activity of Daily Living scale for Amyotrophic Lateral Sclerosis and other motor neuron disorders (PADL-ALS) to support large pragmatic trials. Methods: The Greater Plains Collaborative Clinical Data Research Network (GPC) developed and tested the feasibility of using the PADL-ALS.  We convened patient and caregiver focus groups and in-person meetings to recommend changes to the ALS Functional Rating Scale-Revised (ALSFRS-R), which clarified language and added questions about pseudobulbar affect, pain, and faith.  Feasibility was determined by conducting a survey of participants identified using EMR-computable phenotypes and returned via patient-preferred modalities. Results: Surveys were distributed to 1079 participants at nine GPC health systems.  The survey response rate was 44.4% (range 12.9-57.66%): male to female ratio 1.56; 84% self-identified as a patient with ALS.  Patient respondents used computers or tablets more frequently than caregivers responding on their behalf.  The PADL-ALS correlated to clinic-performed ALSFRS-R within 4 weeks of survey completion (n=33, rho=0.93, Kansas only).  The pseudobulbar affect question correlated to functional motor burden.  Over 80% agreed to be contacted for future research opportunities. Conclusion:  We demonstrated the feasibility of determining functional burden with the PADL-ALS using an EMR-computable phenotype.  Future directions include implementing the PADL-ALS to answer pragmatic questions about ALS care

SEINE: Methods for Electronic Data Capture and Integrated Data Repository Synthesis with Patient Registry Use Cases

Integrated Data Repositories (IDR) allow clinical research to leverage electronic health records (EHR) and other data sources while Electronic Data Capture (EDC) applications often support manually maintained patient registries. Using i2b2 and REDCap, (IDR and EDC platforms respectively) we have developed methods that integrate IDR and EDC strengths supporting: 1) data delivery from the IDR as ready-to-use registries to exploit the annotation and data collection capabilities unique to EDC applications; 2) integrating EDC managed registries into data repositories allows investigators to use hypothesis generation and cohort discovery methods. This round-trip integration can lower lag between cohort discovery and establishing a registry. Investigators can also periodically augment their registry cohort as the IDR is enriched with additional data elements, data sources, and patients. We describe our open-source automated methods and provide three example registry uses cases for these methods: triple negative breast cancer, vertiginous syndrome, cancer distress

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BackgroundPatient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.ObjectivesWe sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.MethodsWe compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites.ResultsSeveral attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001).ConclusionsOverall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Using a computerized provider order entry system to meet the unique prescribing needs of children: description of an advanced dosing model

<p>Abstract</p> <p>Background</p> <p>It is well known that the information requirements necessary to safely treat children with therapeutic medications cannot be met with the same approaches used in adults. Over a 1-year period, Duke University Hospital engaged in the challenging task of enhancing an established computerized provider order entry (CPOE) system to address the unique medication dosing needs of pediatric patients.</p> <p>Methods</p> <p>An advanced dosing model (ADM) was designed to interact with our existing CPOE application to provide decision support enabling complex pediatric dose calculations based on chronological age, gestational age, weight, care area in the hospital, indication, and level of renal impairment. Given that weight is a critical component of medication dosing that may change over time, alerting logic was added to guard against erroneous entry or outdated weight information.</p> <p>Results</p> <p>Pediatric CPOE was deployed in a staggered fashion across 6 care areas over a 14-month period. Safeguards to prevent miskeyed values became important in allowing providers the flexibility to override the ADM logic if desired. Methods to guard against over- and under-dosing were added. The modular nature of our model allows us to easily add new dosing scenarios for specialized populations as the pediatric population and formulary change over time.</p> <p>Conclusions</p> <p>The medical needs of pediatric patients vary greatly from those of adults, and the information systems that support those needs require tailored approaches to design and implementation. When a single CPOE system is used for both adults and pediatrics, safeguards such as redirection and suppression must be used to protect children from inappropriate adult medication dosing content. Unlike other pediatric dosing systems, our model provides active dosing assistance and dosing process management, not just static dosing advice.</p