We developed a clinical tool comprising patient risk factors for having an abnormal calcium (Ca), magnesium (Mg) or phosphate (PO4) level. We hypothesized that patients without a risk factor do not ...require testing. This study examined the tool's potential utility for rationalizing Ca, Mg and PO4 ordering in the emergency department (ED).
We undertook a retrospective observational study in a single metropolitan ED. Patients aged 18 years or more who presented between July and December 2019 were included if they had a Ca, Mg or PO4 test during their ED stay. Demographic and clinical data, including the presence of risk factors, were extracted from the medical record. The primary outcome was a clinically significant abnormal Ca, Mg or PO4 level (>0.2 mmol/l above or below the laboratory reference range).
Calcium, Mg and PO4 levels were measured on 1426, 1296 and 1099 patients, respectively. The positive and negative predictive values and likelihood ratios of the tool identifying a patient with a Ca level > 0.2 mmol/l outside the range were 0.05, 0.99, 1.59 and 0.41, respectively. The values for Mg were 0.02, 1.00, 1.44 and 0.35 and those for PO4 were 0.15, 0.93, 1.38 and 0.57, respectively. The majority of patients not identified as having an abnormal level did not receive electrolyte correction treatment. Application of the tool would have resulted in a 35.8% cost reduction.
The tool failed to predict a very small proportion of patients (approximately 1%) with an abnormal Ca or Mg level and for whom it would have been desirable to have these levels measured. It may help rationalize Ca and Mg ordering and reduce laboratory costs.
•The usefulness of calcium, magnesium and phosphate testing in the emergency department setting has been questioned.•Using clinical factors associated with abnormal levels, we developed a clinical tool to rationalize their testing.•The tool has very high negative predictive values for calcium and magnesium testing.•The tool's performance is less robust for phosphate testing.•Application of the tool has the potential to decrease testing of these electrolytes and to reduce cost.
The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing ...informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.
The HerediGene Population Study is a large research study focused on identifying new genetic biomarkers for disease prevention, diagnosis, prognosis, and development of new therapeutics. A ...substantial IT infrastructure evolved to reach enrollment targets and return results to participants. More than 170,000 participants have been enrolled in the study to date, with 5.87% of those whole genome sequenced and 0.46% of those genotyped harboring pathogenic variants. Among other purposes, this infrastructure supports: (1) identifying candidates from clinical criteria, (2) monitoring for qualifying clinical events (e.g., blood draw), (3) contacting candidates, (4) obtaining consent electronically, (5) initiating lab orders, (6) integrating consent and lab orders into clinical workflow, (7) de-identifying samples and clinical data, (8) shipping/transmitting samples and clinical data, (9) genotyping/sequencing samples, (10) and re-identifying and returning results for participants where applicable. This study may serve as a model for similar genomic research and precision public health initiatives.
