The effect of electronic ordering on pre-analytical errors in primary care

A Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors Work

OBJECTIVES

We aimed to create a library of logic models for interventions to reduce diagnostic error. This library can be used by those developing, implementing, or evaluating an intervention to improve patient care, to understand what needs to happen, and in what order, if the intervention is to be effective.

METHODS

To create the library, we modified an existing method for generating logic models. The following five ordered activities to include in each model were defined: preintervention; implementation of the intervention; postimplementation, but before the immediate outcome can occur; the immediate outcome (usually behavior change); and postimmediate outcome, but before a reduction in diagnostic errors can occur. We also included reasons for lack of progress through the model. Relevant information was extracted about existing evaluations of interventions to reduce diagnostic error, identified by updating a previous systematic review.

RESULTS

Data were synthesized to create logic models for four types of intervention, addressing five causes of diagnostic error in seven stages in the diagnostic pathway. In total, 46 interventions from 43 studies were included and 24 different logic models were generated.

CONCLUSIONS

We used a novel approach to create a freely available library of logic models. The models highlight the importance of attending to what needs to occur before and after intervention delivery if the intervention is to be effective. Our work provides a useful starting point for intervention developers, helps evaluators identify intermediate outcomes, and provides a method to enable others to generate libraries for interventions targeting other errors.

Laboratory test ordering in inpatient hospitals: a systematic review on the effects and features of clinical decision support systems

Background

Studies have revealed inappropriate laboratory testing as a source of waste. This review aimed at evaluating the effects and features of CDSSs on physicians' appropriate laboratory test ordering in inpatient hospitals.

Method

Medline through PubMed, SCOPUS, Web of Science, and Cochrane were queried without any time period restriction. Studies using CDSSs as an intervention to improve laboratory test ordering as the primary aim were included. The study populations in the included studies were laboratory tests, physicians ordering laboratory tests, or the patients for whom laboratory tests were ordered. The included papers were evaluated for their outcomes related to the effect of CDSSs which were categorized based on the outcomes related to tests, physician, and patients. The primary outcome measures were the number and cost of the ordered laboratory tests. The instrument from The National Heart Lung and Blood Institute (NIH) was used to assess the quality of the included studies. Moreover, we applied a checklist for assessing the quality and features of the CDSSs presented in the included studies. A narrative synthesis was used to describe and compare the designs and the results of included studies.

Result

Sixteen studies met the inclusion criteria. Most studies were conducted based on a quasi-experimental design. The results showed improvement in laboratory test-related outcomes (e.g. proportion and cost of tests) and also physician-related outcomes (e.g. guideline adherence and orders cancellation). Patient-related outcomes (e.g. length of stay and mortality rate) were not well investigated in the included studies. In addition, the evidence about applying CDSS as a decision aid for interpreting laboratory results was rare.

Conclusion

CDSSs increase appropriate test ordering in hospitals through eliminating redundant test orders and enhancing evidence-based practice. Appropriate testing and cost saving were both affected by the CDSSs. However, the evidence is limited about the effects of laboratory test CDSSs on patient-related outcomes.

Laboratory testing in primary care: A systematic review of health IT impacts

INTRODUCTION

Laboratory testing in primary care is a fundamental process that supports patient management and care. Any breakdown in the process may alter clinical information gathering and decision-making activities and can lead to medical errors and potential adverse outcomes for patients. Various information technologies are being used in primary care with the goal to support the process, maximize patient benefits and reduce medical errors. However, the overall impact of health information technologies on laboratory testing processes has not been evaluated.

OBJECTIVES

To synthesize the positive and negative impacts resulting from the use of health information technology in each phase of the laboratory 'total testing process' in primary care.

METHODS

We conducted a systematic review. Databases including Medline, PubMed, CINAHL, Web of Science and Google Scholar were searched. Studies eligible for inclusion reported empirical data on: 1) the use of a specific IT system, 2) the impacts of the systems to support the laboratory testing process, and were conducted in 3) primary care settings (including ambulatory care and primary care offices). Our final sample consisted of 22 empirical studies which were mapped to a framework that outlines the phases of the laboratory total testing process, focusing on phases where medical errors may occur.

RESULTS

Health information technology systems support several phases of the laboratory testing process, from ordering the test to following-up with patients. This is a growing field of research with most studies focusing on the use of information technology during the final phases of the laboratory total testing process. The findings were largely positive. Positive impacts included easier access to test results by primary care providers, reduced turnaround times, and increased prescribed tests based on best practice guidelines. Negative impacts were reported in several studies: paper-based processes employed in parallel to the electronic process increased the potential for medical errors due to clinicians' cognitive overload; systems deemed not reliable or user-friendly hampered clinicians' performance; and organizational issues arose when results tracking relied on the prescribers' memory.

DISCUSSION

The potential of health information technology lies not only in the exchange of health information, but also in knowledge sharing among clinicians. This review has underscored the important role played by cognitive factors, which are critical in the clinician's decision-making, the selection of the most appropriate tests, correct interpretation of the results and efficient interventions.

CONCLUSIONS

By providing the right information, at the right time to the right clinician, many IT solutions adequately support the laboratory testing process and help primary care clinicians make better decisions. However, several technological and organizational barriers require more attention to fully support the highly fragmented and error-prone process of laboratory testing.

Significant Reduction in Preanalytical Errors for Nonphlebotomy Blood Draws After Implementation of a Novel Integrated Specimen Collection Module

OBJECTIVES

Most preanalytical errors at our institution occur during nonphlebotomy blood draws. We implemented an electronic health record (EHR), interfaced the EHR to the laboratory information system, and designed a new specimen collection module. We studied the effects of the new system on nonphlebotomy preanalytical errors.

METHODS

We used an electronic database of preanalytical errors and calculated the number and type of the most common errors in the emergency department (ED) and inpatient nursing for 3-month periods before (August-October 2014) and after (August-October 2015) implementation. The level of staff compliance with the new system was also assessed.

RESULTS

The average monthly preanalytical errors decreased significantly from 7.95 to 1.45 per 1,000 specimens in the ED (P < 0001) and 11.75 to 3.25 per 1,000 specimens in inpatient nursing (P < 0001). The rate of decrease was similar for mislabeled, unlabeled, wrong specimen received and no specimen received errors. Most residual errors (80% in the ED and 67% in inpatient nursing) occurred when providers did not use the new system as designed.

CONCLUSIONS

Implementation of a customized specimen collection module led to a significant reduction in preanalytical errors. Improved compliance with the system may lead to further reductions in error rates.