Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

Use of the WISN method to assess the health workforce requirements for the high-volume clinical biochemical laboratories

BACKGROUND

The clinical laboratory services, as an essential part of health care, require appropriate staff capacity to assure satisfaction and improve outcomes for both patients and clinical staff. This study aimed to apply the Workload Indicators of Staffing Need (WISN) method for estimating required laboratory staff requirements for the high-volume clinical biochemical laboratories.

METHODS

In 2019, we applied the WISN method in all 13 laboratories within the Center for Medical Biochemistry of the University Clinical Centre of Serbia (CMB UCCS). A review of annual routinely collected statistics, laboratory processes observations, and structured interviews with lab staff helped identify their health service and additional activities and duration of these activities. The study outcomes were WISN-based staff requirements, WISN ratio and difference, and a recommendation on the new staffing standards for two priority laboratory workers (medical biochemists and medical laboratory technicians).

RESULTS

Medical biochemists' and laboratory technicians' annual available working time in 2019 was 1508 and 1347 working hours, respectively, for the workload of 1,848,889 samples. In general, the staff has four health service, eight support, and 15 additional individual activities. Health service activities per sample can take from 1.2 to 12.6 min. Medical biochemists and medical laboratory technicians spend almost 70% and more than 80% of their available working time, undertaking health service activities. The WISN method revealed laboratory workforce shortages in the CMB (i.e. current 40 medical biochemists and 180 medical laboratory technicians as opposed to required 48 medical biochemists and 206 medical laboratory technicians). Workforce maldistribution regarding the laboratory workload contributes to a moderate-high workload pressure of medical biochemists in five and medical laboratory technicians in nine organizational units.

CONCLUSIONS

The WISN method showed mainly a laboratory workforce shortages and workload pressure in the CMB UCCS. WISN is a simple, easy-to-use method that can help decision-makers and policymakers prioritize the recruitment and equitable allocation of laboratory workers, optimize their utilization, and develop normative guidelines in the field of clinical laboratory diagnostics. WISN estimates require periodic reviews.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background

Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.

Objective

The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.

Methods

We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.

Results

Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).

Conclusions

IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

Technology-Enabled Outreach to Patients Taking High-Risk Medications Reduces a Quality Gap in Completion of Clinical Laboratory Testing

Clinical laboratory quality improvement (QI) efforts can include population test utilization. The authors used a health care organization's Medical Data Warehouse (MDW) to characterize a gap in guideline-concordant laboratory testing recommended for safe use of antirheumatic agents, then tested the effectiveness of laboratory-led, technology-enabled outreach to patients at reducing this gap. Data linkages available through the Kaiser Permanente Colorado MDW and electronic health record were used to identify ambulatory adults taking antirheumatic agents who were due/overdue for alanine aminotransferase (ALT), aspartate aminotransferase (AST), complete blood count (CBC), or serum creatinine (SCr) testing. Outreach was implemented using an interactive voice response system to send patients text or phone call reminders. Interrupted time series analysis was used to estimate reminder effectiveness. Rates of guideline-concordant testing and testing timeliness in baseline vs. intervention periods were determined using generalized linear models for repeated measures. Results revealed a decrease in percentage of 3763 patients taking antirheumatic agents due/overdue for testing at any given time: baseline 24.3% vs. intervention 17.5% (P < 0.001). Among 3205 patients taking conventional antirheumatic agents, concordance for all ALT testing was baseline 52.8% vs. intervention 65.4% (P < 0.001) among patients chronically using these agents and baseline 20.6% vs. intervention 26.1% (P < 0.001) among patients newly starting these agents. The 95^th percentiles for days to ALT testing were baseline 149 vs. intervention 117 among chronic users and baseline 134 vs. intervention 92 among new starts. AST, CBC, and SCr findings were similar. Technology-enabled outreach reminding patients to obtain laboratory testing improves health care system outcomes.