Abstract:Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians' physical and cognitive workload and improve patient safety. Order sets represent time intervalclustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians' cognitiv… Show more
“…They often enable improved adherence to evidence-based guidelines and providing cognitive assistance that leads to error reduction. [18][19][20] Despite the accumulating evidence, the weak result of our intervention highlights the challenges of designing and implementing an effective CDS tool that ade-quately addresses the five rights of CDS. 21 To the extent of our knowledge, the superiority of an interruptive versus noninterruptive intervention has not been well established.…”
Objectives
Reduction in unnecessary services is one strategy for increasing the value of health care. Reference laboratory, or send-out, tests are associated with considerable costs. We investigated whether displaying cost and turnaround time (TAT), or time-to-result, for reference laboratory tests at the time of order entry in the electronic health record system (EHR) would impact provider ordering practices.
Methods
Reference laboratory test cost and TAT data were randomized prior to the study and only displayed for the intervention group. A 24-month data set comprised of 12 months each for baseline and study periods was extracted from the clinical data mart. A difference-in-differences (DID) analysis was conducted using a linear mixed-effects model to estimate the association between the intervention and changes in test ordering patterns.
Results
In the inpatient setting, the DID of aggregate test order costs and volume were not different among the control and intervention groups (P = 0.31 and P = 0.26, respectively). In the ambulatory setting, the DID of aggregate test order costs and volume were not different among the control and intervention groups (P = 0.82 and P = 0.51, respectively). For both inpatient and ambulatory settings, no significant difference was observed in the DID of aggregate test order costs and volumes calculated in respect to stratified relative cost and TAT groups (P > 0.05).
Conclusions
Lack of alternative tests, test orders placed at a late step in patient management, and orders facilitated by trainees or mid-level providers may have limited the efficacy of the intervention. Our randomized study demonstrated no significant association between the display of cost or TAT display and ordering frequency.
“…They often enable improved adherence to evidence-based guidelines and providing cognitive assistance that leads to error reduction. [18][19][20] Despite the accumulating evidence, the weak result of our intervention highlights the challenges of designing and implementing an effective CDS tool that ade-quately addresses the five rights of CDS. 21 To the extent of our knowledge, the superiority of an interruptive versus noninterruptive intervention has not been well established.…”
Objectives
Reduction in unnecessary services is one strategy for increasing the value of health care. Reference laboratory, or send-out, tests are associated with considerable costs. We investigated whether displaying cost and turnaround time (TAT), or time-to-result, for reference laboratory tests at the time of order entry in the electronic health record system (EHR) would impact provider ordering practices.
Methods
Reference laboratory test cost and TAT data were randomized prior to the study and only displayed for the intervention group. A 24-month data set comprised of 12 months each for baseline and study periods was extracted from the clinical data mart. A difference-in-differences (DID) analysis was conducted using a linear mixed-effects model to estimate the association between the intervention and changes in test ordering patterns.
Results
In the inpatient setting, the DID of aggregate test order costs and volume were not different among the control and intervention groups (P = 0.31 and P = 0.26, respectively). In the ambulatory setting, the DID of aggregate test order costs and volume were not different among the control and intervention groups (P = 0.82 and P = 0.51, respectively). For both inpatient and ambulatory settings, no significant difference was observed in the DID of aggregate test order costs and volumes calculated in respect to stratified relative cost and TAT groups (P > 0.05).
Conclusions
Lack of alternative tests, test orders placed at a late step in patient management, and orders facilitated by trainees or mid-level providers may have limited the efficacy of the intervention. Our randomized study demonstrated no significant association between the display of cost or TAT display and ordering frequency.
“…5 The end result of cognitive overload is the inability to recall information, discern irrelevant information from relevant information, and complete a task. 26 The nurse-to-nurse change-of-shift handoff communication is susceptible to cognitive overload due to two primary factors: the healthcare system and what is being communicated in terms of data, information, and knowledge. Healthcare systems have evolved into sociotechnical systems requiring the nurses to process human-to-human and human-to-computer interactions simultaneously to complete a workflow task.…”
Miscommunication occurring during the nursing handoff continues to be a primary cause of sentinel events and adverse patient outcomes. The primary purpose of the nursing handoff is to communicate essential patient data, information, and knowledge to ensure the safe continued continuity of care. The aim of this study was to examine the content of the nurse-to-nurse change-of-shift handoff communication in terms of data, information, and knowledge for both bedside and nonbedside handoffs of a patient who has experienced a clinical event. The setting was an urban medical center on a medical-surgical floor.The sample consisted of one nurse giving and one nurse receiving the handoff (n = 19 registered nurses). Five bedside and five nonbedside handoffs were audio recorded and analyzed using content analysis. The handoff overall contained 34.7% data, 51.7% information, and 13.6% knowledge. The nonbedside handoff compared with the bedside handoff contained a substantially higher percentage of data and less information. The percentage of knowledge being communicated in both the nonbedside and bedside handoff was low at 13.6% and 13.7%, respectively. The percentage of data compared with the percentage of knowledge in the handoff places the nurses at greater risk of experiencing cognitive lapses due to cognitive overload.
“…Electronic order sets can improve adherence to evidencebased practices, reduce variation in care, and minimize cognitive load. [10][11][12] However, poorly designed order sets can unintentionally and negatively impact practice patterns 13 and/or can lead to medical errors. 14 The "five rights" framework for CDS from Osheroff et al notes that good decision support must provide the right information to the right person in the right format through the right channel at the right time in workflow.…”
Background Medical errors in blood product orders and administration are common, especially for pediatric patients. A failure modes and effects analysis in our health care system indicated high risk from the electronic blood ordering process.
Objectives There are two objectives of this study as follows:(1) To describe differences in the design of the original blood product orders and order sets in the system (original design), new orders and order sets designed by expert committee (DEC), and a third-version developed through user-centered design (UCD).(2) To compare the number and type of ordering errors, task completion rates, time on task, and user preferences between the original design and that developed via UCD.
Methods A multidisciplinary expert committee proposed adjustments to existing blood product order sets resulting in the DEC order set. When that order set was tested with front-line users, persistent failure modes were detected, so orders and order sets were redesigned again via formative usability testing. Front-line users in their native clinical workspaces were observed ordering blood in realistic simulated scenarios using a think-aloud protocol. Iterative adjustments were made between participants. In summative testing, participants were randomized to use the original design or UCD for five simulated scenarios. We evaluated differences in ordering errors, time on task, and users' design preference with two-sample t-tests.
Results Formative usability testing with 27 providers from seven specialties led to 18 changes made to the DEC to produce the UCD. In summative testing, error-free task completion for the original design was 36%, which increased to 66% in UCD (30%, 95% confidence interval [CI]: 3.9–57%; p = 0.03). Time on task did not vary significantly.
Conclusion UCD led to substantially different blood product orders and order sets than DEC. Users made fewer errors when ordering blood products for pediatric patients in simulated scenarios when using the UCD orders and order sets compared with the original design.
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