Breast Cancer Care in Kenya – A Hybrid Approach to Clinical Data Management (original) (raw)
Related papers
Leveraging EHR data for outcomes and comparative effectiveness research in oncology
Current oncology reports, 2012
Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effectively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the beginning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Similar efforts are required to address current challenges in sharing EHR data.
2016
Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effec-tively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the begin-ning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Sim-ilar efforts are required to address current challenges in sharing EHR data.
Developing evidence-based tools from EHR data
Nursing Management (Springhouse), 2014
Early in our nursing school journey, we all faced the daunting task of learning how to document patient care. We learned that documentation is important for team communication and serves as a legal document if needed. Despite these important functions, documentation is often seen by nurses as a required, yet time-consuming, task that isn't generally a highly valued part of patient care. Nurses would much rather spend time with their patients. However, with the advent of the electronic health record (EHR), documentation has taken on a new meaning and holds extended value. Data, documented by the clinical team and retrieved electronically, provide the content for quality measurement and research. As more evidence-based assessment tools are translated into practice, nurse managers and their staff members are uniquely positioned to lead or join the effort to make a difference with nursing documentation and EHR design. We have an opportunity to leverage the daily task of documentation into an activity that yields valuable practice evidence, decision support, and communication tools. As an example, our research team is using nursing documentation to build decision support for an important and common process-discharge planning and care coordination. With our 20-year history of using EHR data to build decision support for discharge referral decision making, our story yields valuable insights and lessons learned for nurse managers looking to leverage the EHR to derive evidence-based tools and position nurses to make a difference through documentation. From EHR to decision The nursing admission assessment is a common example of nursing documentation. It occurs upon admission for every admitted patient regardless of setting, but it's quite extensive in acute and home care. Nurses spend 20 minutes to 1 hour or more asking questions, completing assessments, and documenting their findings in the process of admission. However, this rich data source is much underused and stands as an example of how nursing can lead the way in leveraging EHR data to improve care.
BMC medical informatics and decision making, 2011
We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commerciallyavailable EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined "meaningful use" criteria.
Evaluating the Reliability of EHR-Generated Clinical Outcomes Reports: A Case Study
eGEMs (Generating Evidence & Methods to improve patient outcomes), 2014
Introduction: Quality incentive programs, such as Meaningful Use, operate under the assumption that clinical quality measures can be reliably extracted from EHRs. Safety Net providers, particularly Federally Qualified Health Centers and Look-Alikes, tend to be high adopters of EHRs; however, recent reports have shown that only about 9% of FQHCs and Look-Alikes were demonstrating meaningful use as of 2013. Our experience working with the Crescent City Beacon Community (CCBC) found that many health centers relied on chart audits to report quality measures as opposed to electronically generating reports directly from their EHRs due to distrust in the data. This paper describes a step-by-step process for improving the reliability of data extracted from EHRs to increase reliability of quality measure reports, to support quality improvement, and to achieve alignment with national clinical quality reporting requirements.
Main Barriers for Quality Data Collection in EHR - A Review
2011
The volume of health data is rising and health information technologies which include electronic health records are a promising solution, on data management and collection, to achieve greater quality outcomes. However, they often cause errors instead of preventing them. To study the main barriers to high quality data collection from electronic health records, a qualitative review study was conducted using 5 different database engines having only considered data quality and documentation issues, opportunities and challenges for proper data collection, electronic health records data and corresponding databases quality. It were included 16 articles from which data availability, format, accuracy and data accessibility were the most focused problems to address. Still, solutions are available: early recognition of those problems, well structured and designed EHRs, standard coding use, periodic accuracy monitoring and feedback and broad use of such systems for the most daily tasks possible...
BMC Bioinformatics, 2019
Background: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future. Results: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the inhouse EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.