Reducing Unnecessary Phlebotomy Testing Using a Clinical Decision Support System

Clinical Decision Support System in laboratory medicine

Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS.

Reducing Overuse of 3-Day Repeat Type and Screen Testing across an 11-Hospital Safety Net System

BACKGROUND

According to the American Association of Blood Banks, a Type and Screen (T&S) is valid for up to three calendar days. Beyond a limited number of clinical indications such as a transfusion reaction, repeat T&S testing within 3 days is not warranted. Inappropriate repeat T&S testing is a costly medical waste and can lead to patient harm.

OBJECTIVE

To reduce inappropriate duplicate T&S testing across a large, multihospital setting.

SETTING

The largest urban safety net health system in the USA, with 11 acute care hospitals.

INTERVENTIONS

Our first intervention involved adding the time elapsed since the last T&S order into the order and the process instructions that described when a T&S was indicated. The second intervention was a best practice advisory that triggered when T&S was ordered before the expiration of an active T&S.

MAIN MEASURES

The primary outcome measure was the number of duplicate inpatient T&S per 1000 patient days.

KEY RESULTS

Across all hospitals, the weekly average rate of duplicate T&S ordering decreased from 8.42 to 7.37 per 1000 patient days (12.5% reduction, p < 0.001) after the first intervention and to 4.32 per 1000 patient days (48.7% reduction, p < 0.001) after the second intervention. Using linear regression to compare pre-intervention to post-intervention 1, the level difference was - 2.46 (9.17 to 6.70, p < 0.001) and slope difference was 0.0001 (0.0282 to 0.0283, p = 1). For post-intervention 1 to post-intervention 2, the level difference was - 3.49 (8.06 to 4.58, p < 0.001) and slope difference was - 0.0428 (0.0283 to - 0.0145, p < 0.05).

CONCLUSIONS

Our intervention successfully reduced duplicate T&S testing using a two-pronged electronic health record intervention. The success of this low effort intervention across a diverse health system provides a framework for similar interventions in various clinical settings.

Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice

OBJECTIVE

Sustainable investment in computerized decision support systems (CDSS) requires robust evaluation of their economic impacts compared with current clinical workflows. We reviewed current approaches used to evaluate the costs and consequences of CDSS in hospital settings and presented recommendations to improve the generalizability of future evaluations.

MATERIALS AND METHODS

A scoping review of peer-reviewed research articles published since 2010. Searches were completed in the PubMed, Ovid Medline, Embase, and Scopus databases (last searched February 14, 2023). All studies reported the costs and consequences of a CDSS-based intervention compared with current hospital workflows. Findings were summarized using narrative synthesis. Individual studies were further appraised against the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist.

RESULTS

Twenty-nine studies published since 2010 were included. Studies evaluated CDSS for adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). All studies evaluated costs from a hospital perspective but varied based on the valuation of resources affected by CDSS implementation, and the measurement of consequences. We recommend future studies follow guidance from the CHEERS checklist; use study designs that adjust for confounders; consider both the costs of CDSS implementation and adherence; evaluate consequences that are directly or indirectly affected by CDSS-initiated behavior change; examine the impacts of uncertainty and differences in outcomes across patient subgroups.

DISCUSSION AND CONCLUSION

Improving consistency in the conduct and reporting of evaluations will enable detailed comparisons between promising initiatives, and their subsequent uptake by decision-makers.

Status of phlebotomy tube utilization at a major medical center. Are we using too many phlebotomy tubes?

Background and objectives

Overutilization of phlebotomy tubes at healthcare facilities leads to iatrogenic anemia, patient dissatisfaction, and increase in operational costs. In this study, we analyzed the phlebotomy tube usage data at the Zhongshan Hospital, Fudan University, to show potential inefficiencies with phlebotomy tube usage.

Methods

Data of 984,078 patients with 1,408,175 orders and 4,622,349 total phlebotomy tubes were collected during years 2018-2021. Data of different patient types were compared. Furthermore, we assessed the data from subspecialty and test levels to explore the factors influencing the increase in phlebotomy tube usage.

Results

We observed an overall 8% increase in both the mean number of tubes used and blood loss per order over the past 4 years. The mean blood loss per day for intensive care unit (ICU) patients was 18.7 ml (maximum 121.6 ml), which was well under the 200 ml/day threshold. However, the maximum number of tubes used reached more than 30 tubes/day.

Conclusions

The 8% increase of phlebotomy tubes over 4 years should alarm laboratory managements, as tests offered are expected to increase in the future. Importantly, the whole healthcare community needs to work together to solve this problem with more creative solutions.

Cost-effectiveness of routine type and screens in select urological surgeries

PURPOSE

To evaluate the cost-effectiveness of obtaining a preoperative type and screen (T/S) for common urologic procedures.

METHODS

A decision tree model was constructed to track surgical patients undergoing two preoperative blood ordering strategies as follows: obtaining a preoperative T/S versus not doing so. The model was applied to the National (Nationwide) Inpatient Sample (NIS) data, from January 1, 2006 to September 30, 2015. Cost estimates for the model were created from combined patient-level data with published costs of a T/S, type and crossmatch (T/C), a unit of pRBC, and one unit of emergency-release transfusion (ERT). The primary outcome was the incremental cost per ERT prevented, expressed as an incremental cost-effectiveness ratio (ICER) between the two preoperative blood ordering strategies. A cost-effectiveness analysis determined the ICER of obtaining preoperative T/S to prevent an emergency-release transfusion (ERT), with a willingness-to-pay threshold of $1,500.00.

RESULTS

A total of 4,113,144 surgical admissions from 2006 to 2015 were reviewed. The overall transfusion rate was 10.54% (95% CI, 10.17-10.91) for all procedures. The ICER of preoperative T/S was $1500.00 per ERT prevented. One-way sensitivity analysis demonstrated that the risk of transfusion should exceed 4.12% to justify preoperative T/S.

CONCLUSION

Routine preoperative T/S for radical prostatectomy (rate = 3.88%) and penile implants (rate = .91%) does not represent a cost-effective practice for these surgeries. It is important for urologists to review their institution T/S policy to reduce inefficiencies within the preoperative setting.

Towards effective clinical decision support systems: A systematic review

Background

Clinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time.

Objective

To identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1—What are the most common trend characteristics in a CDSS? RQ2—What is the maturity level of the CDSS based on the decision-making theory proposed by Simon?

Methods

AIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review.

Results

The outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity.

Conclusion

The trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon’s decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.