Improving the Accuracy of Code Status Documentation in Home Care.

Advance care planning discussions require specialized skills to elicit goals and preferences from patients contending with life-limiting illness. Documentation forms which include Health Care Proxies, Medical Orders for Life Sustaining Treatments, or Physician Orders for Life Sustaining Treatments are meant to accompany patients through every transition of care. However, they are often forgotten between the hospital and the home setting. Home care clinicians have the obligation to ensure all providers involved in the patient's care are made aware of their code status and goals of care. Consequently, home care clinicians need education about advance care planning to support patients in achieving their care goals as they transition from hospital to home. This quality improvement project implemented three consecutive interventions including reminding clinicians to review code status orders, applying short educational interventions at daily nursing huddles via email, and finally, administering primary palliative education classes for home care clinicians. The purpose was to guide home care nurses in reviewing and reaffirming code status orders and advance care documentation at the initiation of the home care episode and to improve the consistency and accuracy of code status documentation at the transition of care. After implementing the interventions to improve code status documentation, compliance improved from 8% to 100% in a 10-month period.

Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations.

BACKGROUND: Data quality issues in clinical trials can be caused by a variety of behaviors including fraud, misconduct, intentional or unintentional noncompliance, and significant carelessness. Regardless of how these behaviors are defined, they may compromise the validity of the study results. Reliable study results and quality data are needed to evaluate products for marketing approval and for decisions that are made on the use of medicine. This article focuses on detecting data quality issues, irrespective of origin or motive. Early detection of data quality issues are important so that corrective actions taken can be implemented during the conduct of the trial, recurrence can be prevented, and data quality can be preserved. METHODS: A survey was distributed to TransCelerate member companies to assess current strategies for detecting and mitigating risks involving fraud and misconduct in clinical trials. A review of literature across many industries from 1985 to 2014 was conducted using multiple platforms. RESULTS: Eighteen TransCelerate member companies anonymously responded to the survey. All of the respondents had one or more existing strategies for fraud and misconduct detection. The literature search identified current practices and methodologies across many industries. CONCLUSIONS: TransCelerate recommends the creation of an integrated, multifaceted approach to proactively detect data quality issues. Detection methods should include a strategy tailored to the characteristics of the study. Some sponsors are taking advantage of more advanced methods and integrated processes and systems to proactively detect and address issues, relying on advances in technology to more efficiently review data in real time. Further research is underway to assess statistical data quality detection methodology in clinical trials.

Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials.

TransCelerate has developed a risk-based monitoring methodology that transforms clinical trial monitoring from a model rooted in source data verification (SDV) to a comprehensive approach leveraging cross-functional risk assessment, technology, and adaptive on-site, off-site, and central monitoring activities to ensure data quality and subject safety. Evidence suggests that monitoring methods that concentrate on what is critical for a study and a site may produce better outcomes than do conventional SDV-driven models. This article assesses the value of SDV in clinical trial monitoring via a literature review, a retrospective analysis of data from clinical trials, and an assessment of major and critical findings from TransCelerate member company internal audits. The results support the hypothesis that generalized SDV has limited value as a quality control measure and reinforce the value of other risk-based monitoring activities.