Identification of the essential components of quality in the data collection process for public health information systems

The Military-Civilian Partnership Quality Improvement Program Concept: A Process to Improve Data Collection and Outcomes Assessment

INTRODUCTION

Military-Civilian Partnerships (MCPs) are vital for maintaining the deployment readiness of military health care physicians. However, tracking their clinical activity has proven to be challenging. In this study, we introduce a locally driven process aimed at the passive collection of external clinical workload data. This process is designed to facilitate an assessment of MCP physicians' deployment readiness and the effectiveness of individual MCPs.

MATERIALS AND METHODS

From March 2020 to February 2023, we conducted a series of quality improvement projects at the Wright Patterson Medical Center (WPMC) to enhance our data collection efforts for MCP physicians. Our methodology encompassed several steps. First, we assessed our existing data collection processes and their outcomes to identify improvement areas. Next, we tested various data collection methods, including self-reporting, a web-based smart phone application, and an automated process based on billing or electronic health record data. Following this, we refined our data collection process, incorporating the identified improvements and systematically tracking outcomes. Finally, we evaluated the refined process in 2 different MCPs, with our primary outcome measure being the collection of monthly health care data.

RESULTS

Our examination at the WPMC initially identified several weaknesses in our established data collection efforts. These included unclear responsibility for data collection within the Medical Group, an inadequate roster of participating MCP physicians, and underutilization of military and community resources for data collection. To address these issues, we implemented revisions to our data collection process. These revisions included establishing clear responsibility for data collection through the Office of Military-Civilian Partnerships, introducing a regular "roll call" to match physicians to MCP agreements, passively collecting data each month through civilian partner billing or information technology offices, and integrating Office of Military-Civilian Partnership efforts into regular executive committee meetings. As a result, we observed a 4-fold increase in monthly data capture at WPMC, with similar gains when the refined process was implemented at an Air Force Center for the Sustainment of Trauma and Readiness Skills site.

CONCLUSIONS

The Military-Civilian Partnership Quality Improvement Program concept is an effective, locally driven process for enhancing the capture of external clinical workload data for military providers engaged in MCPs. Further examination of the Military-Civilian Partnership Quality Improvement Program process is needed at other institutions to validate its effectiveness and build a community of MCP champions.

Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy

BACKGROUND

Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to apply the Adjusted Morbidity Groups (AMG) algorithm in the context of an Italian Region, and evaluate its performance to support decision-making processes in healthcare programming.

METHODS

The pilot study used five Healthcare Administrative Databases (HADs) covering the period 2015-2021. An iterative semi-automated procedure was developed to extract, filter, check and merge the data. A technical manual was developed to describe the process, designed to be standardized, reproducible and transferable. AMG algorithm was applied and descriptive analysis performed. A dashboard structure was developed to exploit the results of the tool.

RESULTS

AMG produced information on the health status of Marche citizens, highlighting the presence of chronic conditions from age 45 years. Persons with high and very high level of complexity showed elevated mortality rates and an increased use of healthcare resources. A visualization dashboard was intended to provide to relevant stakeholders accessible, updated and ready-to-use aggregated information on the health status of citizens and additional insight on the use of the healthcare services and resources by specific groups of citizens.

CONCLUSION

The flexibility of the AMG, together with its ability to support policymakers and clinical sector, could favour its implementation in different scenarios across Europe. A clear strategy for the adoption of HRA tools and related key elements and lessons learnt for a successful transferability at the EU level were defined. HRA strategies should be considered a pillar of healthcare policies and programming to achieve person-centred care and promote the sustainability of the EU healthcare systems.

From Scientific Research to Practical Implementations: Applications to Improve Data Quality in Child Welfare

Child welfare decisions have life-impacting consequences which, often times, are underpinned by limited or inadequate data and poor quality. Though research of data quality has gained popularity and made advancements in various practical areas, it has not made significant inroads for child welfare fields or data systems. Poor data quality can hinder service decision-making, impacting child behavioral health and well-being as well as increasing unnecessary expenditure of time and resources. Poor data quality can also undermine the validity of research and slow policymaking processes. The purpose of this commentary is to summarize the data quality research base in other fields, describe obstacles and uniqueness to improve data quality in child welfare, and propose necessary steps to scientific research and practical implementation that enables researchers and practitioners to improve the quality of child welfare services based on the enhanced quality of data.

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Introduction

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.

Objective

This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS.

Method

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.

Results

This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed.

Discussions

Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements.

Conclusion

The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

Status of the health information system in Ireland and its fitness to support health system performance assessment: a multimethod assessment based on stakeholder involvement

Background

Between 2019 and 2021, the first Irish health system performance assessment (HSPA) framework was developed. As routinely collected health data are necessary to continuously populate indicators of an HSPA framework, a purpose-driven assessment of the health information system (HIS) in Ireland and its fitness to support the implementation of an HSPA framework was conducted. This study reports on the status of the Irish HIS through a multimethod assessment based on continuous broad stakeholder involvement.

Methods

Between May and November 2020, over 50 informants were engaged in individual and group interviews and stakeholder consultation workshops as part of the HIS assessment process. Descriptive themes and high-level data availability heatmaps were derived from interview and workshop data using thematic analysis. Indicator “passports” for the HSPA framework were populated during stakeholder consultation workshops and analysed using univariate descriptive statistics.

Results

The HIS in Ireland was able to provide administrative, survey and registry-based data for public sector acute care services, focusing on structure, process and output metrics. Significant data availability gaps, most notably from primary care, private hospitals and community care, were reported, with little availability of electronic health record and people-reported data. Data on outcome metrics were mostly missing, as were linkage possibilities across datasets for care pathway monitoring. The COVID-19 pandemic highlighted the national HIS’s shortcomings but also the capacity for rapid development and improvement.

Conclusions

A tailor-made assessment of the HIS in Ireland, involving a broad set of relevant stakeholders, revealed strengths, weaknesses and areas for improvement in the Irish health data landscape. It also contributed to the development of a national HSPA framework and momentum to further strengthen data infrastructure and governance, while working towards a more data-driven and person-centred healthcare system. This work demonstrates the utility of an inclusive HIS assessment process and is applicable beyond Ireland, where this case study was conducted.

Validation of 4D Components for Measuring Quality of the Public Health Data Collection Process: Elicitation Study

Background

Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice.

Objective

This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework.

Methods

Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework.

Results

A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices.

Conclusions

This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction.