SipNose-topiramate: a potential novel approach to binge eating management.

BACKGROUND: Binge-eating disorder) BED) is the most common eating disorder in the United-States. Daily, orally administered topiramate has shown BED treatment efficacy, with two major limitations: frequent and severe side effects and slow time-to-effect. SipNose is a novel non-invasive intranasal direct nose-to-brain drug delivery platform that delivers drugs to the central nervous system consistently and rapidly. Herein, we study a SipNose-topiramate combination product, as an acute "as needed" (PRN) solution for BED management. METHODS: First, SipNose-topiramate's pharmacokinetics (PK) and safety was evaluated. The second part aimed to demonstrate its PRN-treatment feasibility in terms of usability and potential efficacy in reducing the number of binge-eating events. Twelve BED patients were studied over three time periods; 2-weeks of baseline monitoring [BL], 8-weeks of treatment [TX], and 2-weeks of follow up [FU]. RESULTS: The PK profile showed peak plasma levels at 90 min post-administration, a t1/2 > 24 h and consistent topiramate delivery with no adverse events. In the second part, 251 treatments were self-administered by the patient participants. There was a significant reduction from baseline to treatment periods in mean weekly binge-eating events and binge-eating event days per week. This was maintained during the follow up period. Efficacy was corroborated by improved patient illness severity scales. There were no adverse events associated with any administered treatments. Patients were exposed to less drug when compared with accepted oral dosing. CONCLUSIONS: This study introduces a SipNose-topiramate drug-device combination as a potentially safe, effective, and controlled method for BED management. Its findings introduce a potential approach to BED management both as an intranasal and as a PRN therapy for reducing binge-eating events, with a large-scale reduction in patient drug exposure and side effects and with improved patient quality of life. Further studies are needed with larger patient populations to establish SipNose-topiramate as a mainstream treatment for BED. TRIAL REGISTRATION: Registration number and date of registration of the clinical studies reported in this article are as follows: 0157-18-HMO, August 15th 2018 and 6814-20-SMC, December 2nd 2020.

Binge eating disorder (BED) is a common eating disorder. Daily oral topiramate treatment has shown efficacy in clinical studies and off-label use, with frequent and severe side effects. SipNose is a novel, rapid and consistent direct nose-to-brain drug delivery platform. This study evaluates a SipNose-topiramate combination product, as an innovative acute "as needed" (PRN) BED treatment solution. SipNose-topiramate's pharmacokinetics (PK) and safety demonstrated consistent, dose-dependent topiramate delivery with no adverse events. SipNose-topiramate was studied vis-à-vis its safety and feasibility as a PRN-treatment for reducing the number of binge-eating events. 12 BED patients were studied (2-weeks baseline monitoring, 8-weeks treatment, 2-weeks follow-up). Patients were instructed to self-administer the drug when they feel an urge to binge-eat. Two hundred fifty-one treatments were administered. When compared with daily oral dosing, lower doses were used with no adverse events and minimal side effects. Baseline to treatment periods showed significant reduction in mean weekly binge-eating events and binge-eating event days-per-week. This was maintained during follow-up. Improved illness severity scales corroborated the improved feasibility outcomes. In conclusion, this study introduces SipNose-topiramate as a potential "as needed" intranasal treatment for BED that is safe, effective, and reduces drug exposure and side effects. Additional studies are needed to validate SipNose-topiramate as a BED management therapy.

Validation of the Algorithmic Prediction of Failure Modes in Health Care Methodology: Applied to the Department of Sterile Supply and Equipment.

Failure mode and effect analysis (FMEA) is a leading tool for risk management in health care. The term "blanket" approach FMEA describes a comprehensive simultaneous look at the variety of interrelated factors that may directly and indirectly affect patient safety. Applying FMEA with the "blanket" approach is not common, due to FMEA's limitations. Algorithmic prediction of failure modes in health care (APFMH) is leaner and enables the application of the "blanket" approach, but, like FMEA, it lacks formal validation. The authors set out to validate the APFMH method while applying a "blanket" approach. They analyzed the sterile supply handling at a 1900-bed academic medical center. The study's first step took place in the operating room (OR) aspect of the process. An APFMH analysis was performed using the "blanket" approach, to identify the hazards and define the common root causes for predicted hazards. The second step took place a year later at the sterile supply and equipment department (SSED) and aimed to validate these root causes, thus validating the reliability of APFMH. The "blanket" approach analysis with the APFMH method consisted of categorization into 3 risk-dimensions: patient safety, equipment damage, and time management. Root causes were defined for 8 high-ranking hazards. All the root causes for failures, identified by APFMH at the OR department, were revealed as actual hazards in the processes of the SSED. The independent findings at the SSED level validated the list of identified hazards that was formed at the target department (ie, the OR). APFMH methodology is a lean in time and human resources process that ensures comprehensive hazard analysis, which can include the "blanket" approach, and which was validated in this study. The authors suggest using the APFMH methodology for any organizational analysis method that requires the inclusion of "blanket" approaches.

A non-invasive direct nose to brain drug delivery platform vs. invasive brain delivery approach: patient-centered care impact analysis.

Current literature lacks structured methodologies for analyzing medical technologies' impact from the patient-centered care perspective. This study introduces, applies and validates 'Patient-Centered Care Impact Analysis' (PCIA) as a method for identifying patient-centered care associated demands and expectations for a particular technology and assessing its compliance with these demands. PCIA involves five stages: (1) demand identification, (2) ranking demands' impact magnitude, (3) scoring demand compliance (DC), (4) demand priority (DP) assignment based on impact magnitude and compliance, (5) generating a summative impact priority number (IPN). PCIA was performed as a comparative assessment of two central nervous system (CNS) drug-delivery platforms; SipNose, a novel noninvasive Direct-Nose-to-Brain (DNTB), vs. the standard-of-care invasive intrathecal/intracerebroventricular injection (Invasive I/I). Study participants included a ranking team (RT) without experience with the SipNose technology that based their scoring on experimental data; and a validation team (VT) experienced with the SipNose platform. All had experience with, or knowledge of, InvasiveI/I. Demand identification and impact magnitude were performed by one content and one assessment expert. Each participant assessed each technology's DC. DP scores, IPN's and IPN DNTB:InvasiveI/I ratios were generated for each technology, for each team, based on DC and summative DP scores, respectively. Both teams assigned DNTB higher DC scores, resulting in higher DNTB DP, IPN scores and DNTB:InvasiveI/I IPN ratios. Lack of difference between team assessments of DP and IPN ratio validate PCIA as an assessment tool capable of predicting patient-centered clinical care quality for a new technology. The significant differences between the platforms highlight SipNose's patient-care centered advantages as an effective CNS drug-delivery platform.

Perceived reliability of medical device alarms-a major determinant of medical errors driven by frozen medical thinking.

This concept paper introduces the phenomenon of self-assigning a 'perceived reliability' value to medical device readings as a potential source of cognitive bias in medical decision-making. Medical errors can result from clinical decisions based on partial clinical data despite medical device readings providing data to the contrary. At times, this results from clinician distrust of medical device output. Consequentially, clinicians engage in a form of 'frozen thinking', a fixation on a particular thought process despite data to the contrary. Many medical devices, such as intensive care unit (ICU) monitors and alarms, lack validated statistics of device output reliability and validity. In its absence, clinicians assign a self-perceived reliability value to device output data and base clinical decisions therefrom. When the perceived reliability value is low, clinicians distrust the device and ignore device readings, especially when other clinical data are contrary. We explore the cognitive and theoretical underpinnings of this 'perceived reliability' phenomenon. The mental assignment of a perceived reliability value stems from principles of 'script theory' of medical decision-making. In this conceptual framework, clinicians make decisions by comparing current situations to mental 'scripts' of prior clinical decisions and their outcomes. As such, the clinician utilizes scripts of prior experiences to create the perceived reliability value. Self-assigned perceived reliability is subject to multiple dangers of reliability and cognitive biases. Some of these biases are presented. Among these is the danger of dismissing device readings as 'noise'. This is particularly true of ICU alarms that can emit frequent false alarms and contribute to clinician sensory overload. The cognitive dangers of this 'noise dismissal' are elaborated via its similarity to the phenomenon of 'spatial disorientation' among aviation pilots. We conclude with suggestions for reducing the potential bias of 'perceived reliability'. First presented are regulatory/legislative and industry-based interventions for increasing the study of, and end-user access to, validated device output reliability statistics. Subsequently, we propose strategies for overcoming and preventing this phenomenon. We close with suggestions for future research and development of this 'perceived reliability' phenomenon.

Applying health-six-sigma principles helps reducing the variability of length of stay in the emergency department.

BACKGROUND: Reducing length of stay (LOS) is one of the urgent problems in healthcare systems worldwide. Popular methods that are used to reduce LOS are the Lean and the 6 Sigma, which in practice result in limited improvements. In this paper, we introduce and test a tailored method for implementing the 6 Sigma principles in healthcare (we call H-6S). OBJECTIVE: To reduce the variability in the time between admission and discharge of patients in the emergency department. METHODS: The study took place within the emergency department (ED) of Josephtal Medical Center in Eilat, Israel. Our analysis focused on the processes of examining and treating patients from admission to ED until discharge home. The analysis was done during the second quarter of 2018. The implementation of the recommendations took place during Q3 2018. The reported results are from Q3 2018 to Q2 2019, compared to the corresponding period in 2017 (experienced team). RESULTS: In Q2 2017, LOS was 2.42 ± 2.07 h (experienced team, n = 9928). In Q2 2018, the LOS was 2.62 ± 7.04 h (before the H-6S, inexperienced team, n = 9484). In Q2 2019 following the intervention, it reached 2.3 ± 1.74 h (n = 7647). The differences between the standard deviations of the three periods are significant. CONCLUSION: Implementing H-6S dropped the variance of LOS within 3 months and remained low for the whole year. Each new team of physicians who enter the ED should be thoroughly instructed as to the routines and expectations of the system from them, which should narrow the differences of previous education between them.

Algorithmic prediction of failure modes in healthcare.

BACKGROUND: Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. OBJECTIVES: To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. METHODS: The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented. RESULTS: APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. CONCLUSION: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

Unstructured brainstorming is not enough: structured brainstorming based on four verification and validation questions yields better hazard identification in healthcare.

OBJECTIVES: (1) To introduce the Methodical Hazard Identification Checklist (MHIC) for structured brainstorming and the four V&V categories on which it is based, and (2) to compare its efficacy with that of brainstorming (BS) in identifying hazards in healthcare. DESIGN: Comparative analysis of MHIC and team BS results. SETTING: Baruch Padeh Medical Center, Poriya, Israel. STUDY PARTICIPANTS: Quality engineering students, facilitators, validation teams and hospital staff who were familiar with the specific processes. INTERVENTION(S): The number of hazards identified by team BS were compared with those deduced by applying the four V&V hazard categories to each step (the MHIC) of 10 medical and 12 administrative processes. MAIN OUTCOME MEASURE(S): The total number of hazards (1) identified by BS, (2) identified by MHIC, (3) validated by the validation team and (4) hazards identified by both methods that the validation team deemed unreasonable. RESULTS: MHIC was significantly more successful than BS in identifying all hazards for the 22 processes (P < 0.0001). The estimated probabilities of success for BS for administrative and medical processes were 0.4444, 95%CI = [0.3506, 0.5424] and 0.3080, 95%CI = [0.2199, 0.4127], respectively. The estimated probabilities of success for MHIC for administrative and medical processes were 0.9885, 95%CI = [0.9638, 0.9964] and 0.9911, 95%CI = [0.9635, 0.9979], respectively. CONCLUSIONS: Compared to traditional BS, MHIC performs much better in identifying prospective hazards in the healthcare system. We applied MHIC methodology to administrative and medical processes and believe it can also be used in other industries that require hazard identification.

Better patient safety: implementing exploration and exploitation learning in daily medical practice.

BACKGROUND: Current medical daily practice relies on guidelines, protocols and procedures (GPPs), which require exploitation. However, diagnosis, treatment, risk management and process improvements require exploration. Physician are often unable to switch between exploitation and exploration. This study tested a new approach to facilitate switching that included (1) a new 'thinking protocol' that encouraged leaping from exploitation to exploration and (2) a GPP that encouraged leaping from exploration to exploitation. METHOD: Two hundred students were divided into four groups. The groups received a set of tasks that required switching between exploitation and exploration. Three groups received the thinking protocol, the GPP, or both, and the fourth group served as control. RESULTS: With each additional task, all groups increased exploitative tendency(p<0.0001). The two groups with the thinking protocol leaped from exploitation to exploration (p<0.0001); the other two groups remained in exploitation (p=0.1173, p=0.0758). For the groups that employed exploration learning, the group that received the GPP switched back to exploitation (p<0.0001), but the other group remained in exploration (p=0.2363). CONCLUSION: Despite the importance of timely leaping between exploration and exploitation, in some events, medical teams fail to make the appropriate leap. We suggest to use our novel approach and to encourage the leaping between exploration and exploitation in daily medical practice, to enable the prevention of medical errors and to enhance the effectiveness of risk managements and process improvements.

De-freezing frozen patient management.

OBJECTIVE: To compare the effectiveness of two methods in encouraging the consideration of a leap from one patient management routine to another: (i) real-time review of the facts by an external medical team (ii) implementation of the 're-thinking-protocol' ('de-Freezing') by both treating and external medical teams. DESIGN: Students accompanied doctors, nurses and patients as non-interrupting observers. When an obvious gap between the expected and actual findings occurred, it was discussed four times: by two teams (treating team, external medical team) in two discussion modes (real-time review, de-Freezing-questionnaire). The students then recorded if a leap was considered for each discussion. SETTING: The study was conducted in the emergency department of the Baruch Padeh Medical Centre, Poriya, Israel. PARTICIPANTS: All patients were included during times when both medical teams (treating, external) were present. INTERVENTION(S): During 14 periods of 5-7 h each, 459 patients were sampled. In 183 patients, 200 gaps were discovered. RESULTS: The external team considered a leap 76 times, compared with 47 by the treating team (P < 0.001). Using the de-Freezing-protocol, the treating team considered a leap 133 times. Interestingly, even the external team benefited from the de-Freezing protocol and considered a leap 140 times (NS compared to the treating team). CONCLUSIONS: While the importance of timely leaping from one patient management routine to another is emphasized in the training of physicians, medical teams too often fail to do so. The de-Freezing-protocol inexpensively encourages the consideration of a leap beyond what is evoked by the involvement of an external team. The protocol is applicable to all medical processes and should be incorporated into medical practice and education.

Stop! Check your initial assumptions: Frozen patient management in obstetrical practice.

At times, leaping from one patient management routine to an alternative one may be required to mitigate medical errors. "Frozen patient management" is the resultant situation, when, in the face of an obvious gap between the expected and the actual phenomena, leaping from current patient management to an alternative one is not considered or done. Frozen patient management can lead to a significant delay of the correct definitive intervention, be it surgical or pharmacological. The significance of this delay is especially important in time-dependent dynamic situations. In delivery ward, this may cost the life of either the fetus or the mother.In this study, we describe a sequence in which frozen patient management occurred in the delivery ward. Using "thinking protocol" (herein termed "de-freezing" questionnaire) made the team stop and consider a leap when gaps became apparent, and saved the mother's life.We believe that adopting the "de-freezing questionnaire" as a routine adjunct for all medical activities would lead to a timely change of treatment line, which, in turn, will save lives and unnecessary suffer.

Time to follow guidelines, protocols, and structured procedures in medical care and time to leap out.

Present medical practice encourages management according to written guidelines, protocols, and structured procedures (GPPs). Daily medical practice includes instances in which "leaping" from one patient management routine to another is a must. We define "frozen patient management", when patient management leaping was required but was not performed. Frozen patient management may cause significant damage to patient safety and health and the treatment quality. This paper discusses the advantages and disadvantages of GPP-guided medical practice and gives an explanation of the problem of frozen patient management in light of quality engineering, control engineering, and learning processes. Our analysis of frozen patient management is based on consideration of medical care as a process. By considering medical care processes as a closed-loop control process, it is possible to explain why, when an indication for deviation from the expected occurs, it does not necessarily attract the medical teams' attention, thereby preventing the realization that leaping to an alternative patient management is needed. We suggest that working according to GPPs intensifies the frozen patient management problem since working according to GPPs relates to "exploitation learning behavior", while leaping to new patient management relates to "exploration learning behavior". We indicate practice routines to be incorporated into GPP-guided medical care, to reduce frozen patient management.