This research was underpinned by an imperative to carefully monitor the impact that electronic ordering has on the functioning of pathology laboratory services and their contribution to safe and quality patient care.55 It highlights the importance of using quantitative analyses built upon robust evidence-based performance indicators as a means of encouraging transparency and clarity about what is being achieved and the desired outcome.56 The comparative empirical findings that emerge from the benefits realisation framework can identify what works best, where, and in what circumstances, as a means of enhancing the implementation and sustainability of electronic ordering systems and maximising their contribution to safe and high quality patient care.
Contribution to evidence-based practice: Evidence-based medicine has meant a shift in the culture of health provision away from decisions based on opinion, past practices and precedent towards a system that better utilises science, research and evidence to guide decision making.57 For pathology, this has inspired a new emphasis on its role in the whole patient journey beginning with asking the right clinical questions about the selection of the most appropriate test or investigation to diagnose a problem, to the interpretation and provision of clinical advice, and treatment across the whole spectrum of clinical specialties involved in the patient pathway.58
Quality and safety of patient care: The World Health Organization’s World Alliance for Patient Safety has highlighted the importance of pathology services to the global patient safety agenda emphasising the role of the laboratory in: i) ensuring reliable and accurate results delivered in a timely fashion; ii) informing clinical management decisions; and iii) the safe administration of blood products and medications.59 The main sources of laboratory errors arise within the pre-analytic (clinician’s test order and CSR) and post-analytic (laboratory report to the clinician) phases of the process. It is in these areas where electronic ordering can have a major positive impact. Electronic decision support functions can assist clinicians to improve the quality of test ordering, for example selecting appropriate tests, accurately specifying all aspects of the test order including relevant clinical information, and indicating clinical urgency. It can also help to promote appropriate test ordering and utilisation that facilitates quality decision making and health benefit for the patient.9
Effectiveness of pathology services: There is some evidence, from general practice and acute care settings over the last decade, of the potential for electronic ordering to improve the effectiveness of health care,60-62 promote compliance with evidence-based guidelines63 and accentuate the use of evidence to support clinical decision making.64 However, the utilisation of electronic ordering in Australia and overseas has yet to extend beyond a small number of hospitals and the utilisation of decision support functions has not been extensive.11 Moreover, the implementation of electronic ordering represents a potential high risk for hospitals65 that can lead to unexpected outcomes66 and test ordering errors including the over-utilisation or inappropriate ordering of tests.67 One of the main gaps within the existing literature is that it often neglects to compare different applications over time in order to identify the features that contribute to their success or otherwise.11 It also often fails to account for the crucial role that factors like education, feedback and quality improvement can have on the success and sustainability of decision support features.68 69 This means that there is an insufficient understanding of why a system may be useful and effective in one setting but not another.70
The evidence provided by this research has led to a set of indicators that can be used to monitor various aspects of electronic ordering and its effect on the laboratory processes (predominantly the pre-analytical processes). These indicators can be used for comparisons between hospitals, wards etc., to help improve the overall safety of the patient, efficiency in the wards and help improve the quality and value of pathology provided. Tables 22a-22f provide a summary of these indicators which make up the key elements of the Benefits Realisation Framework.
|Quality of pathology test orders and specimens|
|Definition||Quality pathology testing requires accurate patient and test order information as well as safe collection and transport of all specimens to the pathology service.|
|Aim||To accurately document the type of pre-analytical errors (e.g., mislabelled specimens, patient detail problems or unmet collection requirements) and to use this information to address the cause of the errors and to improve the quality of pathology provided by the pathology service.|
|Rationale||Patient safety may be compromised by pre-analytical errors that can occur at any of the many steps that a specimen and test order form take before specimen processing and analysis actually begins.25 71 Electronic ordering has been introduced with the purpose of improving the quality of information provided to the laboratory thus enhancing the safety of the patient and improving efficiency and effectiveness in the laboratory.|
|Potential uses||The measurement of errors can be performed as part of an overall assessment between hospitals, between wards or across time. A comprehensive evaluation of errors allows for complex issues to be assessed and provides a valuable quality improvement tool.|
|Potential confounders||Documentation of errors needs to be part of the routine laboratory procedure. Classification of the various errors is subject to a range of interpretations so clear unambiguous definitions are required.|
|Data sources||All pathology services are required to collect and report laboratory errors as part of the NATA medical testing accreditation requirements.72 Computerised error logs provide data in digital form that is generally more amenable to validity and integrity checks and statistical analyses. The manual intervention required to audit a paper error log is associated with the need for a much greater investment of time and resources.|
Table 22a. Benefits realisation framework: Quality of pathology test orders and specimens.
|Definitioin||The total number of tests ordered for a given period measured through a variety of methods e.g., per test order episode, per patient admission, per Diagnosis-related Group (DRG), per patient admission, and per specific test type (e.g., Troponin).|
|Aim||To compare and monitor test volumes using the metrics described previously.|
|Rationale||Clinical decision support components of electronic ordering have the potential to improve the appropriateness of pathology test ordering. Alternatively, the ease with which an order can be made may also increase the risk of over-ordering pathology tests. The impact of excessive ordering is not just financial; it may lead to an increase in false positives resulting in unnecessary and expensive diagnostic examinations and treatments.73|
|Potential uses||Assessing test volume using a variety of metrics (described above) allows for a comprehensive analysis of test utilisation in the pathology service. For example, assessing test volume per test order episode informs whether changes that make test ordering more accessible (i.e., electronic ordering) are associated with over-ordering; and assessing test volume per patient admission per DRG allows test volume assessments to control for the type, severity, and complexity of the patients’ condition.|
|Potential confounders||Research in this field shows that the volume of test ordering may be affected by a variety of factors including, the type of hospital (i.e., teaching or non-teaching), seniority and position of clinical staff and even by the number of clinicians who see a patient.74 There is often a direct relationship between patient length of stay and the number of tests per patient admission. Test volume for electronic and paper orders cannot be directly compared because, when both methods of ordering are available, they may be used differently in different clinical contexts (i.e., different wards, or patients of differing diagnostic complexity).|
|Data sources||Analyses of test volume per test order episode can be conducted using data extracted from a LIS; however, analyses using other metrics (patient admission and DRG categories) will require a LIS dataset that has been linked with admission, discharge, and DRG data extracted from the PAS and EDIS.75|
Table 22b. Benefits realisation framework: Test volumes.
|Add-on test rates|
|Definition||Add-on tests are tests performed on an existing specimen previously submitted to the pathology service with an earlier test order.34 35|
|Aim||To assess the volume and distribution of add-on tests.|
|Rationale||Add-on tests are labour-intensive and interruptive of the workflow in the laboratory. Add-on test utilisation places a disproportionate burden on laboratory resources.33|
|Potential uses||Understanding the utilisation of add-on testing can assist in decisions regarding the allocation of resources and, potentially, changes in the processes used for add-on testing.39|
|Potential confounders||An add-on test rate can be defined in two ways: (1) the number of add-on tests as a proportion of all tests, and (2) the number of add-on test order episodes (that may contain requests for multiple add-on tests) as a proportion of all test order episodes.|
|Data sources||Data extracted from LIS are sufficient to conduct analyses of add-on test volumes and rates. Analyses are rendered much easier if the LIS supports a binary flag or checkbox to identify add-on tests (rather than free-text).|
Table 22c. Benefits realisation framework: Add-on test rates.
|Definition||While there are many pathology tests that are conducted repeatedly in order to monitor a condition or treatment, when a repeat test is ordered within a brief time frame there is a high likelihood that it will be redundant and will provide no additional information.60 76|
|Aim||To identify the proportion of repeat tests ordered within different time-frames and compare these proportions for paper- and electronically-ordered repeat tests at each hospital.|
|Rationale||Electronic ordering systems allow ordering clinicians to see what tests have already been ordered. They can also provide on-screen warnings suggesting that a repeat test order has been made within an inappropriate time frame. Such information may lead clinicians to decide not to order a repeat test that they otherwise would have ordered.|
|Potential uses||Reduce the rate of inappropriate test orders.|
|Potential confounders||Inappropriate testing is generally used to refer to the ordering of tests without a clear clinical indication or performed at the wrong time or too frequently to be of value in diagnosis or clinical management in line with evidence-based guidelines and expert consensus.38|
|Data sources||One aspect of test appropriateness can be assessed by looking at the temporal properties of repeat testing. For this type of analysis, data extracted from LIS are sufficient, but the analysis should select specific tests and clinical settings.|
Table 22d. Benefits realisation framework: Test appropriateness.
|Definition||Laboratory turnaround time (TAT) is the time taken by the laboratory to complete the testing process (from when the specimen arrives in the CSR to when a result is available to the clinician). It is also possible to analyse the data entry time (from receipt of the specimen at CSR to when the specimen is ready to leave CSR for processing and analysis).|
|Aim||Comparisons between electronic and paper orders of both data entry times and Total Laboratory TAT.|
|Rationale||Clinical satisfaction with pathology services is related to the timeliness of test results because of its effect on time to patient diagnosis and/or treatment.77|
|Potential uses||Electronic ordering is most likely to directly affect the data entry time but may also have flow-on effects on Total Laboratory TAT.|
|Potential confounders||TAT can be affected by a number of factors including the type of test being ordered and transportation requirements.|
|Data sources||Data extracted from LIS should be sufficient for turnaround time analyses.|
Table 22e. Benefits realisation framework: Turnaround times.
|Impact on patient outcomes (ED length of stay):|
|Definition||Length of stay (LOS) represents the amount of time a patient remains in ED from triage to discharge.|
|Aim||To understand what factors associated with pathology testing play a role in a patient’s LOS in the ED.|
|Rationale||EDs are a high-activity and high-demand component of the hospital.78 ED LOS is one of the major factors contributing to hospital overcrowding53 and laboratory TAT is one of the many contributing factors to ED LOS.79 Shorter stays in the EDs are also indicative of efficient diagnosis and stabilisation of the patient condition and, therefore, of the ED’s performance as a whole.47 80 81|
|Potential uses||Quantifying benefits, in patient-experience terms, aids in the resource-allocation strategies in the hospital.|
|Potential confounders||Many ED visits will involve multiple pathology tests ordered across multiple test order episodes. Each of those tests will influence more-or-less strongly the clinicians’ diagnostic decision and treatment; therefore, care should be taken to consider how analyses can utilise the turnaround time of the decision-critical tests.|
|Data sources||Analyses of the impact of various factors on length of stay in ED will require a LIS dataset that has been linked with admission, discharge, triage, and demographic data extracted from the EDIS.|
Table 22f. Benefits realisation framework: Impact on patient outcomes (Length of stay in ED).