Measuring performance in healthcare

Hospitals invest in process management and process optimization from an organizational and patient perspective to increase efficiency and simultaneously the quality of their operations. Consequently, the use of process-oriented performance measurement systems gains importance. This study contributes to the development of a dashboard for the process of hip surgery using a case study design. We integrate strategic goals of hospital management and different stakeholders with the analysis of Business Process Management and Hospital Information Systems’ data. Process-oriented KPIs were integrated into the dashboard using a three-step approach. Dashboards enable healthcare organizations to put process-oriented performance measurement into practice

Business Intelligence Competencies: Making Healthcare Data Come Alive

Business Intelligence Competencies: Making Healthcare Data Come Alive While a wealth of healthcare related data exists, nurse leaders (NL) have yet to understand its implications and adopt analytical skills to lead in the transformation of care delivery. Information science is at a new frontier for nursing to embrace. It is critical for nursing leadership to advance and support business intelligence (BI) and interactive data visualization (IDV) skills across the organization and advocate for greater engagement of nurses in health system decision making. With these new tools and competencies, nursing and other health professions can innovate best practices, providing enhanced quality, safety, and value in healthcare. The aim of this Doctor of Nursing evidence-based project was to engage NL’s to improve and extend competencies in BI and IDV. A survey was administered to NL’s to assess their knowledge and use of these analytic tools and then guide a process for skill development via two workshops presenting an overview of BI and IDV to NL’s. The use of BI is still in its’ infancy, dashboards tools are beginning to be deployed across healthcare organization, however, data in real time is not readily available, nor is the ability to interact and conduct data discovery. The effectiveness of the education program was evaluated by the attendees’ willingness to participate in workshops covering the basic uses of BI and IDV and understanding of the opportunities to incorporate them into their current leadership role

Validation strategy of a bioinformatics whole genome sequencing workflow for Shiga toxin-producing Escherichia coli using a reference collection extensively characterized with conventional methods

Whole genome sequencing (WGS) enables complete characterization of bacterial pathogenic isolates at single nucleotide resolution, making it the ultimate tool for routine surveillance and outbreak investigation. The lack of standardization, and the variation regarding bioinformatics workflows and parameters, however, complicates interoperability among (inter)national laboratories. We present a validation strategy applied to a bioinformatics workflow for Illumina data that performs complete characterization of Shiga toxin-producing Escherichia coli (STEC) isolates including antimicrobial resistance prediction, virulence gene detection, serotype prediction, plasmid replicon detection and sequence typing. The workflow supports three commonly used bioinformatics approaches for the detection of genes and alleles: alignment with blast+, kmer-based read mapping with KMA, and direct read mapping with SRST2. A collection of 131 STEC isolates collected from food and human sources, extensively characterized with conventional molecular methods, was used as a validation dataset. Using a validation strategy specifically adopted to WGS, we demonstrated high performance with repeatability, reproducibility, accuracy, precision, sensitivity and specificity above 95 % for the majority of all assays. The WGS workflow is publicly available as a ‘push-button’ pipeline at https://galaxy.sciensano.be. Our validation strategy and accompanying reference dataset consisting of both conventional and WGS data can be used for characterizing the performance of various bioinformatics workflows and assays, facilitating interoperability between laboratories with different WGS and bioinformatics set-ups

A three-step methodology for process-oriented performance : how to enhance automated data collection in healthcare

Background. Healthcare managers often attempt to enhance process-oriented performance. However, this remains a challenge. New approaches aimed at increasing the implementation success of process-oriented performance measurement should be investigated. Methods. This study investigates and discusses a step-by-step methodology to implement an automated and effective process-oriented performance measurement system in a hospital. The methodology is based on a framework for developing dashboards based on three steps: the demand side, supply side, and the fit between the two. An illustrative case of the process of hip surgery in the operating room of two hospitals is used. Results. A methodology has been developed to define a reliable set of process-oriented performance metrics, allowing analysis and management of the different flows in healthcare in an integrated way, several methods were investigated to automatically integrate the data gathered into a reporting infrastructure that can be used to disseminate the results. Conclusion. This step-by-step methodology allows healthcare organizations to develop and implement effective process-oriented performance measurement in an automated way. This allows the alignment of the goals of hospital management and various stakeholders with the more analytical analysis of business process management notation and hospital information system (HIS) data

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

Introduction:Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. Methods:The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. Results:This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. Conclusions:The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients

Process Innovation With Lightweight It at an Emergency Unit

In this paper, we are studying the role of lightweight IT in process innovation. Our research question is how can lightweight IT support process innovation within an established e-health information infrastructure? Our empirical evidence is a qualitative case study at a primary care emergency service in Oslo. We provide two contributions. First, applying the lens of business process innovation to the literature on information infrastructures, we retain the value of the installed base, while we at the same time ad speed to the implementation project. Second, we demonstrate the role of lightweight technology in improving logistics and message interaction within and between health units. The lightweight technologies availability on the commercial market makes acquisition and implementation faster. Based on this, we briefly suggest a bypassing strategy where a new layer of technology is built separately from the existing infrastructure in order to effectively address process innovation efforts

Focal Spot, Spring 1995

https://digitalcommons.wustl.edu/focal_spot_archives/1069/thumbnail.jp

Driving performance:International studies on performance management of hospital services and health care systems in times of the COVID-19 pandemic and lessons learnt for its aftermath

This thesis explored the field of healthcare performance management and provided insights into key drivers contributing to better performance. Performance management supports reaching individual, organisational, or system goals through setting objectives, developing strategies, organising work, monitoring progress, providing feedback, taking corrective actions, and evaluating outcomes. It is inextricably linked to data and involves managerial and collaborative efforts of healthcare workers in the context of healthcare systems and services. Measurement and management policies and practices across diverse healthcare settings were researched on both hospital and healthcare system levels. With an international scope, most of this work was done during the COVID-19 pandemic, which influenced research focus and methods. The crucial role of data, people and collaboration in organisational and system-level decision-making has been identified and highlighted. Contrasting approaches in utilising performance data were revealed between hospital managers in different geographies. Additionally, the research uncovered innovative, collaborative tools and practices emerging during the pandemic alongside persistent challenges such as data silos and governance issues. This work provides insights into healthcare performance management, guiding future research and policy development toward patient-centric, resilient healthcare. Policy implications stress the importance of investing in managerial training, aligning metrics with desired outcomes, and fostering collaboration for resilient healthcare systems

Agile and Lean Systems Engineering: Kanban in Systems Engineering

This is the 2nd of two reports that were created for research on this topic funded through SERC. The first report, SERC-TR-032-1 dated March 13, 2012, constituted the 2011-2012 Annual Technical Report and the Final Technical Report of the SERC Research Task RT-6: Software Intensive Systems Data Quality and Estimation Research In Support of Future Defense Cost Analysis. The overall objectives of RT-6 were to use data submitted to DoD in the Software Resources Data Report (SRDR) forms to provide guidance for DoD projects in estimating software costs for future DoD projects. In analyzing the data, the project found variances in productivity data that made such SRDR-based estimates highly variable. The project then performed additional analyses that provided better bases of estimate, but also identified ambiguities in the SRDR data definitions that enabled the project to help the DoD DCARC organization to develop better SRDR data definitions. In SERC-TR-2012-032-1, the resulting Manual provided the guidance elements for software cost estimation performers and users. Several appendices provide further related information on acronyms, sizing, nomograms, work breakdown structures, and references. SERC-TR-2013-032-2 (current report), included the “Software Cost Estimation Metrics Manual.” This constitutes the 2012-2013 Annual Technical Report and the Final Technical Report of the SERC Research Task Order 0024, RT-6: Software Intensive Systems Cost and Schedule Estimation Estimating the cost to develop a software application is different from almost any other manufacturing process. In other manufacturing disciplines, the product is developed once and replicated many times using physical processes. Replication improves physical process productivity (duplicate machines produce more items faster), reduces learning curve effects on people and spreads unit cost over many items. Whereas a software application is a single production item, i.e. every application is unique. The only physical processes are the documentation of ideas, their translation into computer instructions and their validation and verification. Production productivity reduces, not increases, when more people are employed to develop the software application. Savings through replication are only realized in the development processes and on the learning curve effects on the management and technical staff. Unit cost is not reduced by creating the software application over and over again. This manual helps analysts and decision makers develop accurate, easy and quick software cost estimates for different operating environments such as ground, shipboard, air and space. It was developed by the Air Force Cost Analysis Agency (AFCAA) in conjunction with DoD Service Cost Agencies, and assisted by the SERC through involving the University of Southern California and the Naval Postgraduate School. The intent is to improve quality and consistency of estimating methods across cost agencies and program offices through guidance, standardization, and knowledge sharing. The manual consists of chapters on metric definitions, e.g., what is meant by equivalent lines of code, examples of metric definitions from commercially available cost models, the data collection and repository form, guidelines for preparing the data for analysis, analysis results, cost estimating relationships found in the data, productivity benchmarks, future cost estimation challenges and a very large appendix.SERCU.S. Department of DefenseSystems Engineering Research Center (SERC)Systems Engineering Research Center (SERC) Contract H98230-08-D-0171