Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review.

BACKGROUND: Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim. METHODS: Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework. RESULTS: Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain. CONCLUSIONS: This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.

Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance.

OBJECTIVE: Vital signs-based models are complicated by repeated measures per patient and frequently missing data. This paper investigated the impacts of common vital signs modeling assumptions during clinical deterioration prediction model development. STUDY DESIGN AND SETTING: Electronic medical record (EMR) data from five Australian hospitals (1 January 2019-31 December 2020) were used. Summary statistics for each observation's prior vital signs were created. Missing data patterns were investigated using boosted decision trees, then imputed with common methods. Two example models predicting in-hospital mortality were developed, as follows: logistic regression and eXtreme Gradient Boosting. Model discrimination and calibration were assessed using the C-statistic and nonparametric calibration plots. RESULTS: The data contained 5,620,641 observations from 342,149 admissions. Missing vitals were associated with observation frequency, vital sign variability, and patient consciousness. Summary statistics improved discrimination slightly for logistic regression and markedly for eXtreme Gradient Boosting. Imputation method led to notable differences in model discrimination and calibration. Model calibration was generally poor. CONCLUSION: Summary statistics and imputation methods can improve model discrimination and reduce bias during model development, but it is questionable whether these differences are clinically significant. Researchers should consider why data are missing during model development and how this may impact clinical utility.

Predictors of acute muscle loss in the intensive care unit: A secondary analysis of an in-bed cycling trial for critically ill patients.

PURPOSE: The purpose of this study was to assist clinicians to identify critically ill patients at greatest risk of acute muscle loss and to analyse the associations between protein intake and exercise on acute muscle loss. MATERIALS AND METHODS: Secondary analysis of a single-centre randomised clinical trial of in-bed cycling using a mixed effects model was undertaken to examine the association between key variables and rectus femoris cross-sectional area (RFCSA). Groups were combined, and key variables for the cohort were modified Nutrition Risk in the Critically Ill (mNUTRIC) scores within the first days following intensive care unit admission, longitudinal RFCSA measurements, percent of daily recommended protein intake, and group allocation (usual care, in-bed cycling). RFCSA ultrasound measurements were taken at baseline and days 3, 7, and 10 to quantify acute muscle loss. All patients received usual care nutritional intake while in the intensive care unit. Patients allocated to the cycling group commenced in-bed cycling once safety criteria were met. RESULTS: Analysis included all 72 participants, of which 69% were male, with a mean (standard deviation) age of 56 (17) years. Patients received a mean (standard deviation) of 59% (26%) of the minimum protein dose recommended for critically ill patients. Mixed-effects model results indicated that patients with higher mNUTRIC scores experienced greater RFCSA loss (estimate = -0.41; 95% confidence interval [CI] = -0.59 to -0.23). RFCSA did not share a statistically significant association with cycling group allocation (estimate = -0.59, 95% CI = -1.53 to 0.34), the percentage of protein requirements received (estimate = -0.48; 95% CI = -1.16 to 0.19), or a combination of cycling group allocation and higher protein intake (estimate = 0.33, 95% CI = -0.76 to 1.43). CONCLUSIONS AND RELEVANCE: We found that a higher mNUTRIC score was associated with greater muscle loss, but we did not observe a relationship between combined protein delivery and in-bed cycling and muscle loss. The low protein doses achieved may have impacted the potential for exercise or nutrition strategies to reduce acute muscle loss. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ACTRN 12616000948493).

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.

Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare.

OBJECTIVE: Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls. MATERIALS AND METHODS: Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance. RESULTS: The proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration. DISCUSSION: Our results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research. CONCLUSIONS: This study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

Inpatient Fall Prediction Models: A Scoping Review.

INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction. METHODS: This scoping review used methods recommended by the Arksey and O'Malley framework and its recent advances. PubMed, CINAHL, IEEE Xplore, and EMBASE databases were systematically searched. Studies reporting the development of inpatient fall risk prediction approaches were included. There was no restriction on language or recency. Reference lists and manual searches were also completed. Reporting quality was assessed using adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement (TRIPOD), where appropriate. RESULTS: Database searches identified 1,396 studies, 63 were included for scoping assessment and 45 for reporting quality assessment. There was considerable overlap in data sources and methods used for model development. Fall prediction models typically relied on features from patient assessments, including indicators of physical function or impairment, or cognitive function or impairment. All but two studies used patient information at or soon after admission and predicted fall risk over the entire admission, without consideration of post-admission interventions, acuity changes or length of stay. Overall, reporting quality was poor, but improved in the past decade. CONCLUSION: There was substantial homogeneity in data sources and prediction model development methods. Use of artificial intelligence, including machine learning with high-dimensional data, remains underexplored in the context of hospital falls. Future research should consider approaches with the potential to utilize high-dimensional data from digital hospital systems, which may contribute to greater performance and clinical usefulness.

Correction: Application of a Mixed Methods Multi-Criteria Decision Analysis Framework in Integrated Health Care.

[This corrects the article DOI: 10.5334/ijic.5997.].

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes.

BACKGROUND: Hospital patients experiencing clinical deterioration are at greater risk of adverse events. Monitoring patients through early warning systems is widespread, despite limited published evidence that they improve patient outcomes. Current limitations including infrequent or incorrect risk calculations may be mitigated by integration into electronic medical records. Our objective was to examine the impact on patient outcomes of systems for detecting and responding to real-time, automated alerts for clinical deterioration. METHODS: This review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. We searched Medline, CINAHL and Embase for articles implementing real-time, automated deterioration alerts in hospitalised adults evaluating one or more patient outcomes including intensive care unit admission, length of stay, in-hospital cardiopulmonary arrest and in-hospital death. RESULTS: Of 639 studies identified, 18 were included in this review. Most studies did not report statistically significant associations between alert implementation and better patient outcomes. Four studies reported statistically significant improvements in two or more patient outcomes, and were the only studies to directly involve the patient's clinician. However, only one of these four studies was robust to existing trends in patient outcomes. Of the six studies using robust study designs, one reported a statistically significant improvement in patient outcomes; the rest did not detect differences. CONCLUSIONS: Most studies in this review did not detect improvements in patient outcomes following the implementation of real-time deterioration alerts. Future implementation studies should consider: directly involving the patient's physician or a dedicated surveillance nurse in structured response protocols for deteriorating patients; the workflow of alert recipients; and incorporating model features into the decision process to improve clinical utility.

Patient and economic impact of implementing a paediatric sepsis pathway in emergency departments in Queensland, Australia.

We examined systems-level costs before and after the implementation of an emergency department paediatric sepsis screening, recognition and treatment pathway. Aggregated hospital admissions for all children aged < 18y with a diagnosis code of sepsis upon admission in Queensland, Australia were compared for 16 participating and 32 non-participating hospitals before and after pathway implementation. Monte Carlo simulation was used to generate uncertainty intervals. Policy impacts were estimated using difference-in-difference analysis comparing observed and expected results. We compared 1055 patient episodes before (77.6% in-pathway) and 1504 after (80.5% in-pathway) implementation. Reductions were likely for non-intensive length of stay (- 20.8 h [- 36.1, - 8.0]) but not intensive care (-9.4 h [- 24.4, 5.0]). Non-pathway utilisation was likely unchanged for interhospital transfers (+ 3.2% [- 5.0%, 11.4%]), non-intensive (- 4.5 h [- 19.0, 9.8]) and intensive (+ 7.7 h, [- 20.9, 37.7]) care length of stay. After difference-in-difference adjustment, estimated savings were 596 [277, 942] non-intensive and 172 [148, 222] intensive care days. The program was cost-saving in 63.4% of simulations, with a mean value of $97,019 [- $857,273, $1,654,925] over 24 months. A paediatric sepsis pathway in Queensland emergency departments was associated with potential reductions in hospital utilisation and costs.

Application of a Mixed Methods Multi-Criteria Decision Analysis Framework in Integrated Health Care.

Background: Evaluating integrated care programs is complex. Integration benefits may not become apparent within short evaluation timeframes, and many programs provide a wide variety of health and non-health benefits. To address these challenges, we illustrate a mixed methods approach for evaluating multiple integrated care programs using multi-criteria decision analysis. Methods: We adapted a decision support tool used by local decision makers to compare data extracted from 17 different integrated care evaluations. Criteria included impact on health services capacity, patient outcomes, integration of care, workforce development and implementation risk, weighted based on stakeholder preferences. Program benefits were compared to their implementation costs, and assessed using cost-effectiveness methods. Sensitivity analysis examined the impact of different criteria weights. Results and discussion: This method captured a diverse range of benefits provided by integrated care programs and provided an accessible heuristic to compare many projects simultaneously. However, this approach may not be sensitive to the appropriateness of each criterion to the health system, the magnitude of difference in individual criteria, equity considerations or socio-political factors. Internal and external validation, especially for subjective criteria such as implementation risk, are needed. Conclusions: This work offers a feasible, flexible and pragmatic approach for evaluating integrated care programs.

A Bayesian Approach for Incorporating the EQ-5D Visual Analog Scale When Estimating the Health-Related Quality of Life.

OBJECTIVES: The EuroQoL 3-level version of EQ-5D and 5-level version of EQ-5D questionnaires are often used to quantify health states. They include ordinal responses across 5 health dimensions (EQ-5D index) and an EQ-visual analog scale (EQ-VAS) overall health rating. We investigated the value of incorporating the EQ-VAS to update health utility estimates using a Bayesian framework. METHODS: We created a joint bivariate normal EQ-VAS and EQ-5D index utility model and compared this to a univariate normal EQ-5D index utility model. We tested these models for 1026 Sri Lankan patients with chronic kidney disease and 94 Australian patients with wounds. We validated our approach by simulating EQ-VAS and EQ-5D index responses and applying our Bayesian model and then comparing the modeled estimates to our observed data. RESULTS: The combined model showed a reduction in estimate uncertainty for all respondents. Compared with the EQ-5D index-only model, the mean utility for Sri Lankan respondents dropped from 0.556 (0.534-0.579) to 0.540 (0.521-0.559) in men and increased from 0.489 (0.461-0.518) to 0.528 (0.506-0.550) in women, with reduced credible interval width by 13% and 23%, respectively. The mean utility in Australian respondents moved from 0.715 (0.633-0.800) to 0.716 (0.652-0.782) in men, and 0.652 (0.581-0.723) to 0.652 (0.593-0.711) in women, with reduced credible interval width by 23% and 17%, respectively. The credible interval width for simulated data also narrowed, ranging from 8.3 to 8.5%. CONCLUSIONS: Including the EQ-VAS through Bayesian methods can add value by reducing requisite sample sizes and decision uncertainty using small amounts of additional data that is often collected but rarely used.

Resource use and costs associated with epilepsy in the Queensland hospital system: protocol for a population-based data linkage study.

INTRODUCTION: Epilepsy places a large burden on health systems, with hospitalisations for seizures alone occurring more frequently than those related to diabetes. However, the cost of epilepsy to the Australian health system is not well understood. The primary aim of this study is to quantify the health service use and cost of epilepsy in Queensland, Australia. Secondary aims are to identify differences in health service use and cost across population and disease subgroups, and to explore the associations between health service use and common comorbidities. METHODS AND ANALYSIS: This project will use data linkage to identify the health service utilisation and costs associated with epilepsy. A base cohort of patients will be identified from the Queensland Hospital Admitted Patient Data Collection. We will select all patients admitted between 2014 and 2018 with a diagnosis classification related to epilepsy. Two comparison cohorts will also be identified. Retrospective hospital admissions data will be linked with emergency department presentations, clinical costing data, specialist outpatient and allied health occasions of service data and mortality data. The level of health service use in Queensland, and costs associated with this, will be quantified using descriptive statistics. Difference in health service costs between groups will be explored using logistic regression. Linear regression will be used to model the associations of interest. The analysis will adjust for confounders including age, sex, comorbidities, indigenous status, and remoteness. ETHICS AND DISSEMINATION: Ethical approval has been obtained through the QUT University Human Research Ethics Committee (1900000333). Permission to waive consent has been granted under the Public Health Act 2005, with approval provided by all relevant data custodians. Findings of the proposed research will be communicated through presentations at national and international conferences, presentations to key stakeholders and decision-makers, and publications in international peer-reviewed journals.

Economic Analysis of Specialist Referral Patterns in Mackay, Queensland Following HealthPathways Implementation.

INTRODUCTION: HealthPathways is a clinical information portal developed in New Zealand that enables general practitioners to manage and refer their patients in a local context. We analyzed specialist outpatient appointment costs in Mackay, Queensland before and after HealthPathways implementation. METHODS: We retrospectively examined specialist outpatient costs for patients referred by Mackay general practitioners for conditions with varying levels of HealthPathways implementation. Ranked from most clinical pathways available to none, chronic diabetes, cardiology, respiratory, and urology visits from January to March 2015, pre-pathways, and January to March 2017, post-pathways, were assessed. Monte Carlo simulation was used to estimate cost changes. Per-visit costs were multiplied by visit numbers to estimate policy impact. RESULTS: The mean cost per visit increased from $220 to $305 for diabetes and $270 to $323 for respiratory, and decreased from $296 to $257 for cardiology and $444 to $293 for urology. The policy impact for each disease group over 3 months after accounting for visit numbers was a likely saving of $30 360 for diabetes and $10 270 for cardiology, and a likely cost increase of $24 449 for respiratory and $20 536 for urology. CONCLUSIONS: We observed that conditions with more comprehensive clinical pathways cost Mackay HHS substantially less following implementation. Costs for low and no pathway implementation referrals increased slightly over the same period.

Adherence to best practice: Preventing surgical site infection following caesarean section in Australia.

BACKGROUND: Surgical site infection (SSI) following caesarean section is a serious but underreported problem with an estimated incidence of 5-9%. It is essential to identify adherence to established prevention strategies to reduce the incidence rate. AIMS: The aims of this study were to quantify unwarranted variation from evidence-based practice on the prevention of SSI at caesarean section in Australia; and to identify predictors of not implementing an existing infection prevention bundle: pre-incision antibiotic prophylaxis, vaginal preparation and spontaneous placenta removal. MATERIALS AND METHODS: An online cross-sectional survey of obstetricians and obstetric Diplomates was conducted in 2016. The primary outcome was adherence to an existing infection prevention bundle, with demographic and clinical variables predicting adherence through multivariable binary logistic regression. RESULTS: Forty-nine percent of respondents (response rate 39.6%) reported implementing zero or only one element of the infection prevention bundle. The types of respondents most likely to have poor adherence were Diplomates (adjusted odds ratio (aOR) 2.58), obstetricians practising in private hospitals (aOR 3.34), those usually practising in public and private hospitals (aOR 2.23), and those not usually implementing a surgical safety checklist (aOR 3.77). CONCLUSIONS: Adherence to best practice at caesarean section is low among many Australian obstetricians. Infection control practitioners and obstetricians need to collaboratively implement surgical safety checklists at caesarean section, and monitor implementation using process key performance indicators, and audit and feedback. These strategies will reduce unwarranted variation from evidence-based infection control practice.

Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis.

BACKGROUND: Hip arthroplasty is increasing in Australia. The number of procedures for fractured neck of femur was 7500 in 2017. Best practices for fixation method and procedure type require scrutiny. This paper is about the costs and health outcomes of cemented and uncemented hemiarthroplasty and total hip arthroplasty at a national level. METHODS: We created a Markov model for patients <75, aged 75-85, and over 85. Expected costs and health outcomes over 5 years from a decision to change from existing practice to a best practice policy in which all patients with fractured neck of femur received the same fixation method based on age and type of arthroplasty are estimated. The model was populated using prevalence and incidence data from the Australian Orthopedic Association National Joint Replacement Registry, costs from Metro North Hospital and Health Service in Queensland, and probabilities and utilities from the literature. We simulated the uncertainties in outcomes with probabilistic sensitivity analysis. RESULTS: We found that uncemented stem procedures were more costly and provided worse health outcomes compared to cemented stem fixation for hemiarthroplasty and total hip arthroplasty for all age groups. Moving from existing practice to cemented stem arthroplasty could save the Australian health system $2.0 million over 5 years with a gain of 203 quality-adjusted life years. CONCLUSION: We suggest that consideration be given to cemented fixation of the femoral stem for patients receiving both hemiarthroplasty and total hip arthroplasty for fractured neck of femur. Best practice guidelines focused on cost-effectiveness should recommend cemented stem fixation to both save costs and improve patient quality of life.

Fits, faints, falls and funny turns: cost and capacity savings in Queensland from the accelerated transient attack pathway initiative (ATAP).

BACKGROUND: falls, seizures, syncope and transient ischaemic attacks (TIA) are common presentations to emergency departments sharing overlapping clinical features and diagnostic uncertainties. These transient attacks can be markers of serious adverse outcomes and are associated with high admission rates. We evaluated the effects of an integrated suite of pathways for transient attacks designed to improve adherence to best practices and reduce costs through fewer admissions. METHODS: a suite of clinician-designed pathways based on initial presenting diagnosis was developed to support ambulant care in a large hospital in Queensland, Australia. We performed a set of regression analyses to identify the differences in total cost and length of stay (LOS) before and after implementation. We conducted a Monte Carlo simulation to estimate the cost savings of the freed capacity in the patient cohort. RESULTS: pathway implementation was associated with reduced admitted LOS and costs. Falls patients admitted LOS declined by 32.5%, and admission costs by 19.5%. Syncope, seizure, and TIA patients admitted LOS declined by 22% with no change in admitted costs. Despite a small increase in 90-day representations, total emergency department LOS was unchanged. Emergency department costs were similar between falls and non-falls patients. The Monte Carlo analysis showed that the most likely outcome was a cost savings in freed capacity of $71 per patient episode. CONCLUSION: the ATAP suite of pathways was associated with reduction in LOS, release of capacity and reduction in costs. Further study is needed to evaluate mechanisms and clinical outcomes in this vulnerable population.

Development and pilot of a multicriteria decision analysis (MCDA) tool for health services administrators.

INTRODUCTION: Health administration is complex and serves many masters. Value, quality, infrastructure and reimbursement are just a sample of the competing interests influencing executive decision-making. This creates a need for decision processes that are rational and holistic. METHODS: We created a multicriteria decision analysis tool to evaluate six fields of healthcare provision: return on investment, capacity, outcomes, safety, training and risk. The tool was designed for prospective use, at the beginning of each funding round for competing projects. Administrators were asked to rank their criteria in order of preference. Each field was assigned a representative weight determined from the rankings. Project data were then entered into the tool for each of the six fields. The score for each field was scaled as a proportion of the highest scoring project, then weighted by preference. We then plotted findings on a cost-effectiveness plane. The project was piloted and developed over successive uses by the hospital's executive board. RESULTS: Twelve projects competing for funding at the Royal Brisbane and Women's Hospital were scored by the tool. It created a priority ranking for each initiative based on the weights assigned to each field by the executive board. Projects were plotted on a cost-effectiveness plane with score as the x-axis and cost of implementation as the y-axis. Projects to the bottom right were considered dominant over projects above and to the left, indicating that they provided greater benefit at a lower cost. Projects below the x-axis were cost-saving and recommended provided they did not harm patients. All remaining projects above the x-axis were then recommended in order of lowest to highest cost-per-point scored. CONCLUSION: This tool provides a transparent, objective method of decision analysis using accessible software. It would serve health services delivery organisations that seek to achieve value in healthcare.

Review of methods and study designs of evaluations related to clinical pathways.

Objective The HealthPathways program is an online information portal that helps clinicians provide consistent and integrated patient care within a local health system through localised pathways for diagnosis, treatment and management of various health conditions. These pathways are consistent with the definition of clinical pathways. Evaluations of HealthPathways programs have thus far focused primarily on website utilisation and clinical users' experience and satisfaction, with limited evidence on changes to patient outcomes. This lack motivated a literature review of the effects of clinical pathways on patient and economic outcomes to inform a subsequent HealthPathways evaluation. Methods A systematic review was performed to summarise the analytical methods, study designs and results of studies evaluating clinical pathways with an economic outcome component published between 1 January 2000 and 31 August 2017 in four academic literature databases. Results Fifty-five relevant articles were identified for inclusion in this review. The practical pre-post study design with retrospective baseline data extraction and prospective intervention data collection was most commonly used in the evaluations identified. Straightforward statistical methods for comparing outcomes, such as the t-test or χ2 test, were frequently used. Only four of the 55 articles performed a cost-effectiveness analysis. Clinical pathways were generally associated with improved patient outcomes and positive economic outcomes in hospital settings. Conclusions Clinical pathways evaluations commonly use pragmatic study designs, straightforward statistical tests and cost-consequence analyses. More HealthPathways program evaluations focused on patient and economic outcomes, clinical pathway evaluations in a primary care setting and cost-effectiveness analyses of clinical pathways are needed. What is known about the topic? HealthPathways is a web-based program that originated from Canterbury, New Zealand, and has seen uptake elsewhere in New Zealand, Australia and the UK. The HealthPathways program aims to assist the provision of consistent and integrated health services through dedicated, localised pathways for various health conditions specific to the health region. Evaluations of HealthPathways program focused on patient and economic outcomes have been limited. What does this paper add? This review synthesises the academic literature of clinical pathways evaluations in order to inform a subsequent HealthPathways evaluation. The focus of the synthesis was on the analytical methods and study designs used in the previous evaluations. The previous clinical pathway evaluations have been pragmatic in nature with relatively straightforward study designs and analysis. What are the implications for practitioners? There is a need for more economic and patient outcome evaluations for HealthPathways programs. More sophisticated statistical analyses and economic evaluations could add value to these evaluations, where appropriate and taking into consideration the data limitations.

Scepticaemia: The impact on the health system and patients of delaying new treatments with uncertain evidence; a case study of the sepsis bundle.

BACKGROUND: A sepsis care bundle of intravenous vitamin C, thiamine, and hydrocortisone was reported to improve treatment outcomes. The data to support it are uncertain and decision makers are likely to be cautious about adopting it. The objective of this study was to model the opportunity costs in dollars and lives of waiting for better information before adopting the bundle. METHODS: A decision tree was built using information from the literature. We modelled the impact of bundle adoption under three scenarios using a simulation in which the bundle was effective as reported in the primary trial, less effective based on other information, and ineffective. The measurements were health services costs, quality-adjusted life years, and transition probabilities. RESULTS: If the bundle proves to be effective under either scenario, it will save billions of dollars and millions of life-years in the United States. This is the opportunity cost of delaying an adoption decision and waiting for better quality evidence. We suggest that hospital decision-makers consider implementing the bundle on a trial basis while monitoring costs and outcomes data even while the evidence base is uncertain. CONCLUSIONS: If the decision maker is unwilling to use the best available evidence now, but rather wishes to wait for definitive evidence they are risking incurring large costs for health care systems and for the patients they serve. An explicit analysis of uncertain clinical outcomes is a useful adjunct to guide decision making where there is clinical ambiguity. This approach offers a valid alternative to the default of clinical inactivity when faced with uncertainty.