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Introduction: Our GI lab had significant wait times for EUS, ERCP, and Colonoscopy with EMR, needing anesthesia support, resulting in diagnostic delays, prolonged length of hospital stay (LOS), and increased no show rates. Methods: Review of workflows associated with both GI outpatient and inpatient procedures revealed multiple inefficiencies. A Pareto diagram regarding inpatient procedures revealed that inadequate prep (~1/3) was most commonly associated with delays. On average access time for outpatient advanced endoscopies was >90 days. Delays resulted in a no show rate of 5%, which was partly also due to inadequate patient notifications. We also realized that existing anesthesia request work flow was inefficient. A major barrier resulting in inefficiency was asynchronous general anesthesia (GA) team and endoscopist work flow. Transportation waste was also a major barrier as PACU (post anesthesia care unit) for recovery of sedation was~ 400 yards away, that required ~ 20 minutes to walk time. Major intervention was implementing the two room GA model on Tuesdays and Wednesdays and synchronizing GA and endoscopist workflows. A new recovery area within the GI lab was created to decrease transport waste. EMR enhancements for anesthesia request, patient notifications, and new inpatient split bowel prep order-set were implemented Outcome measures included total nubmers of cases completed, access time for procedures, bowel prep order-set utilization rate, quality of bowel prep, and time from bowel prep order to procedure as a surrogate for LOS Results: The GI lab throughput increased from 9 GA cases to 20 cases on Tues-Wed (124% increase). The anesthesia cases on days of the week without the two room GA model remained unchanged Average access time for GA referrals decreased from 78.7 days to 52.2 days (33.7% decrease). Access time for all referrals decreased from 75.6 days from 55 days (26%). New split bowel prep order set utilization was 78%. Time to inpatient procedure from order placement was 1 day if the bowel prep order set is used versus 1.3 days if not used. 72% of patients in the bowel prep order set group had excellent prep compared to 36% in the non split prep group. Conclusion: Adopting a DMAIC and lean process model, our multidisciplinary team of endoscopist, anesthesiologists, trainees, nurses, and schedulers significantly improved efficiency and access to cases needing GA in a busy GI Lab

Cancer data from population-based cancer registries under-report cancer cases, especially for cancers primarily diagnosed and treated in outpatient clinical settings, away from hospital-based cancer registrars. Previously, we developed alternative methods of cancer case capture including a claims-based method, which identified a large proportion of cancer cases missed by traditional population-based cancer registries. In this study, we adapted a claims-based method for statewide implementation of cancer surveillance in Florida. Between 2010 and 2017 the claims-based method identified 143,083 cancer abstracts, of which 42% were new and 58% were previously registered. The claims-based method led to the creation of 53,419 new cancer cases in the state cancer registry, which made up 9.3% of all cancer cases registered between 2010 and 2017. The types of cancers identified by the claims-based method were typical of the kinds primarily diagnosed and treated in outpatient oncology clinic settings, such as hematological malignancies, prostate cancer, melanoma, breast cancer, and bladder cancer. These cases were added to the Florida cancer registry and may produce an artefactual increase in cancer incidence, which is believed to be closer to the actual burden of cancer in the state.

There is a large conceptual gap between end-to-end infrastructure requirements and detailed component configuration implementing those requirements. Today, this gap is manually bridged so large numbers of configuration errors are made. Their adverse effects on infrastructure security, availability, and cost of ownership are well documented. This paper presents ConfigAssure to help automatically bridge the above gap. It proposes solutions to four fundamental problems: specification, configuration synthesis, configuration error diagnosis, and configuration error repair. Central to ConfigAssure is a Requirement Solver. It takes as input a configuration database containing variables, and a requirement as a first-order logic constraint in finite domains. The Solver tries to compute as output, values for variables that make the requirement true of the database when instantiated with these values. If unable to do so, it computes a proof of unsolvability. The Requirement Solver is used in different ways to solve the above problems. The Requirement Solver is implemented with Kodkod, a SAT-based model finder for first-order logic. While any requirement can be directly encoded in Kodkod, parts of it can often be solved much more efficiently by non model-finding methods using information available in the configuration database. Solving these parts and simplifying can yield a reduced constraint that truly requires the power of model-finding. To implement this plan, a quantifier-free form, QFF, is defined. A QFF is a Boolean combination of simple arithmetic constraints on integers. A requirement is specified by defining a partial evaluator that transforms it into an equivalent QFF. This QFF is efficiently solved by Kodkod. The partial evaluator is implemented in Prolog. ConfigAssure is shown to be natural and scalable in the context of a realistic, secure and fault-tolerant datacenter.

Some viewers turned to cable, where AMC's \"The Walking Dead\" set records, and FX's \"Sons of Anarchy,\" Showtime's \"Homeland\" and HBO's \"Boardwalk Empire\" joined A&E's \"Duck Dynasty\" and Discovery's \"Gold Rush\" in chipping away viewers.

Some viewers turned to cable, where AMC's \"The Walking Dead\" set records, and FX's \"Sons of Anarchy,\" Showtime's \"Homeland\" and HBO's \"Boardwalk Empire\" joined A&E's \"Duck Dynasty\" and Discovery's \"Gold Rush\" in chipping away viewers.