Medical decision-making is revolutionizing with the introduction of artificial intelligence and machine learning. Yet, traditional algorithms using biomarkers to optimize drug treatment continue to be important and necessary. In this context, early diagnosis and rational antimicrobial therapy of sepsis and lower respiratory tract infections (LRTI) are vital to prevent morbidity and mortality. In this study we report an original cost-effectiveness analysis (CEA) of using a procalcitonin (PCT)-based decision algorithm to guide antibiotic prescription for hospitalized sepsis and LRTI patients versus standard care. We conducted a CEA using a decision-tree model before and after the implementation of PCTguided antibiotic stewardship (ABS) using real-world U.S. hospital-specific data. The CEA included societal and hospital perspectives with the time horizon covering the length of hospital stay. The main outcomes were average total costs per patient, and numbers of patients with Clostridium difficile and antibiotic resistance (ABR) infections. We found that health care with the PCT decision algorithm for hospitalized sepsis and LRTI patients resulted in shorter length of stay, reduced antibiotic use, fewer mechanical ventilation days, and lower numbers of patients with C. difficile and ABR infections. The PCT-guided health care resulted in cost savings of $25,611 (49% reduction from standard care) for sepsis and $3630 (23% reduction) for LRTI, on average per patient. In conclusion, the PCT decision algorithm for ABS in sepsis and LRTI might offer cost savings in comparison with standard care in a U.S. hospital context. To the best of our knowledge, this is the first health economic analysis on PCT implementation using U.S. real-world data. We suggest that future CEA studies in other U.S. and worldwide settings are warranted in the current age when PCT and other decision algorithms are increasingly deployed in precision therapeutics and evidence-based medicine.

We propose a methodology framework for evaluating complex intervention programs on connected care platforms such as remote patient monitoring for populations with long-term conditions in their potential in ROI for health care organizations and in their cost effectiveness for pending market introduction by health systems or payers. Methods: We built a probabilistic decision-analytic model to compare patient-reported costs and outcomes on QALYs of remote patient monitoring in addition to usual care given the published evidence and uncertainty from the Whole Systems Demonstrator Study which was setup in the United Kingdom from 2008 to 2009 [1]. The model was populated with as-reported survivor-specific QALYs distribution and compared to adjusted QALYs to compensate the effect of different mortality rates among the control and intervention group. We computed metrics such as, net monetary benefit, incremental cost effectiveness ratio and acceptability, expected value of perfect information and ROI. Results: The WSD reported an ICER of £92,000 and an CEA of 11% at willingness to pay threshold of £30 000 [1]. Our unadjusted model produced an ICER of £52,300 with an CEA of 38%. The adjusted QALY model produced an ICER of £24,800. At this level, the probability of cost effectiveness increased to 56%. Conclusions: Single-point measurements of QALY in an end-of-life population can cause bias which in turn can influence decision making on cost effectiveness of interventions. We suggest program effectiveness measurements to be taken repeatedly over time, covering the whole cohort at baseline and adjusting for non-survivors during follow-up, by collecting information on defined outcome measures from patients, clinicians, and administrative sources. The selected measures can be translated in the quadruple aim around lowered cost, improved staff experience, improved health outcomes and patient experience. [1] Cost effectiveness of telehealth for patient with long term conditions (Whole Systems Demonstrator study). Henderson et al. BMJ 2013

Objectives: To perform an overview of systematic reviews of economic evaluations of pharmacy services and triangulate results with recommendations for economic evaluations of public health interventions and recommendations for economic evaluations alongside trials. Methods: 1) Exploratory review of recommendations on the economic evaluation of public health interventions; 2) Exploratory review of recommendations for conducting economic evaluations alongside trials; 3) Overview of systematic reviews of economic evaluations of pharmacy interventions in 8 databases. Methods are detailed in protocol registered with PROSPERO (http://www. crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016032768). A threefold critical appraisal of the methodology was performed: 1) Quality of included reviews; 2) Quality of evidence of community pharmacy primary studies reported in reviews; 3) Applicability and transferability. Results: Fourteen systematic reviews containing 75 index publications were included. Reviews reported favorable economic findings for 71% of studies with full economic evaluations. The types of economic analysis are diverse. Two critical quality domains are absent from most reviews. Key findings include: certain types of risk of bias more frequent; wider scope of study designs; most economic quality criteria are met but some issues unresolved or unclear; heterogeneity in populations, interventions and some outcomes; equity not assessed; process dimensions poorly described. Triangulation reveals additional gaps. Conclusions: The economic evaluation of pharmacy interventions presents challenges. Since in recent years, payers made substantial changes in pharmacy remuneration systems, including contracting with Pharmacies and paying for relevant interventions, we hope these findings may assist in improving the design, implementation and assessment of pilot trials, hence the robustness of evidence to justify payers' investment. We also propose a methodological approach for future research in performing economic evaluations of pharmacy-based public health interventions. As research expands, it will become important to build a multidisciplinary expert consensus around a specific guidance on economic evaluation of pharmacy-based public health interventions.