A systematic literature review on unnecessary diagnostic testing: The role of ICT use

Pathology requesting in a regional Australian Emergency Department; an observational study comparing current practice with college guidelines

INTRODUCTION

In 2018, the Australasian College for Emergency Medicine (ACEM) and the Royal College of Pathologists of Australasia (RCPA) produced a guideline to encourage appropriate pathology requesting in the Emergency Department (ED).

OBJECTIVE

To assess adherence to the ACEM/RCPA pathology testing guideline in a regional ED.

METHODS, DESIGN, SETTING AND PARTICIPANTS

This was a retrospective observational study conducted at a regional Australian ED over 7 days. Adults with a presenting complaint encompassed by the guideline were included. All blood tests were audited against the guideline recommendations and classified as indicated or non-indicated. Chi-squared analyses were performed to explore the association between presenting complaint and non-indicated testing.

MAIN OUTCOME MEASURE

The primary outcome was the number of non-indicated blood tests.

RESULTS

Forty percent of tests ordered were not clinically indicated, with non-indicated testing occurring during 87% of encounters. The C-reactive protein (CRP) was the test most frequently ordered outside of guidelines (94% non-indicated). Patients presenting with lower abdominal pain accounted for nearly one-quarter of all non-indicated tests.

CONCLUSIONS

Blood tests were commonly requested outside of the guideline recommendations and interventions to improve pathology stewardship are required.

Expenditure mapping of pediatric imaging costs using a resource utilization band analysis of claims data

OBJECTIVE

To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs.

METHODS

A Claims data for children enrolled in a commercial value-based plan were categorized by RUB 0 non-user, 1 healthy user, 2 low morbidity, 3 moderate morbidity, 4 high morbidity, & 5 very high morbidity. The per member per year (PMPY) expense, total imaging spend, and imaging modality with the highest spend were assessed for each RUB. Diagnosis categories associated with high imaging costs were also evaluated.

RESULTS

There were 40,022 pediatric plan members. 14% had imaging-related claims accounting for approximately $2.8 million in expenditures. Member distribution and mean PMPY expenditure RUB was respectively: RUB 0 (3,037, $0), RUB 1 (6,604, $7), RUB 2 - 13,698, $27), RUB 3 - 13,341, $87), RUB 4 (2,810, $268), RUB 5 (532, $841). RUB 3 had the largest total imaging costs at $1,159,523. The imaging modality with the greatest mean PMPY expense varied by RUB with radiography highest in lower RUBs and MRI highest in higher RUBs. The top 3 diagnoses associated with the highest total imaging costs were developmental disorders ($443,980), asthma ($388,797), and congenital heart disease ($294,977) and greatest mean PMPY imaging expenditures malignancy/leukemia ($3,100), transplant ($2,639), and tracheostomy ($1,661).

DISCUSSION

Expense mapping using claims data allows for a better understanding of the distribution of imaging costs across a covered pediatric population. This tool may assist organizations in planning effective cost-reduction initiatives and learning how imaging utilization varies by patient complexity in their system.

Evaluation of claims data from a commercial value-based insurance product shows pediatric imaging is not a major driver of overall or pediatric healthcare expenditures

BACKGROUND

Initiatives to reduce healthcare expenditures often focus on imaging, suggesting that imaging is a major driver of cost.

OBJECTIVE

To evaluate medical expenditures and determine if imaging was a major driver in pediatric as compared to adult populations.

METHODS

We reviewed all claims data for members in a value-based contract between a commercial insurer and a healthcare system for calendar years 2021 and 2022. For both pediatric (<18 years of age) and adult populations, we analyzed average per member per year (PMPY) medical expenditures related to imaging as well as other categories of large medical expenses. Average PMPY expenditures were compared between adult and pediatric patients.

RESULTS

Children made up approximately 20% of members and 21% of member months but only 8-9% of expenditures. Imaging expenditures in pediatric members were 0.2% of the total healthcare spend and 2.9% of total pediatric expenditures. Imaging expenditures per member were seven times greater in adults than children. The rank order of imaging expenditures and imaging modalities was also different in pediatric as compared to adult members.

CONCLUSION

Evaluation of claims data from a commercial value-based insurance product shows that pediatric imaging is not a major driver of overall, nor pediatric only, healthcare expenditures.

Clinical utility - Information about the usefulness of tests

The clinical utility of a diagnostic test refers to its usefulness in improving patient outcomes, informing clinical decision-making, and optimizing healthcare resources. A diagnostic test with high clinical utility provides accurate, reliable, and actionable information that can guide appropriate treatment decisions, monitor treatment response, and identify potential adverse events or complications. Ultimately, the clinical utility of a diagnostic test depends on how well it can improve patient outcomes by guiding appropriate treatment decisions, improving clinical outcomes, and optimizing healthcare resource utilization. Healthcare providers need to weigh the benefits and drawbacks of using a particular diagnostic test in their clinical practice to determine its clinical utility.

Clinical decision-making framework against over-testing based on modeling implicit evaluation criteria

Different statistical methods include various subjective criteria that can prevent over-testing. However, no unified framework that defines generalized objective criteria for various diseases is available to determine the appropriateness of diagnostic tests recommended by doctors. We present the clinical decision-making framework against over-testing based on modeling the implicit evaluation criteria (CDFO-MIEC). The CDFO-MIEC quantifies the subjective evaluation process using statistics-based methods to identify over-testing. Furthermore, it determines the test's appropriateness with extracted entities obtained via named entity recognition and entity alignment. More specifically, implicit evaluation criteria are defined-namely, the correlation among the diagnostic tests, symptoms, and diseases, confirmation function, and exclusion function. Additionally, four evaluation strategies are implemented by applying statistical methods, including the multi-label k-nearest neighbor and the conditional probability algorithms, to model the implicit evaluation criteria. Finally, they are combined into a classification and regression tree to make the final decision. The CDFO-MIEC also provides interpretability by decision conditions for supporting each clinical decision of over-testing. We tested the CDFO-MIEC on 2,860 clinical texts obtained from a single respiratory medicine department in China with the appropriate confirmation by physicians. The dataset was supplemented with random inappropriate tests. The proposed framework excelled against the best competing text classification methods with a Mean_F₁ of 0.9167. This determined whether the appropriate and inappropriate tests were properly classified. The four evaluation strategies captured the features effectively, and they were imperative. Therefore, the proposed CDFO-MIEC is feasible because it exhibits high performance and can prevent over-testing.