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BACKGROUND The incentive spirometer is a basic and common medical device from which electronic healthcare data cannot be directly collected. As a result, despite numerous studies investigating its utility in clinical care, there remains little consensus on optimal device utility and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications. OBJECTIVE Develop a medical device to capture and use incentive spirometry data. METHODS We designed, built, and tested an add-on device using reflexive optical sensors to identify the real time location of the volume and flow pistons of the incentive spirometer. Data was simultaneously stored internally on the device and transmitted for collection and analysis. An algorithm was developed to determine breath quality to improve validity of data analysis. The device and algorithm were tested for data capture and labeling accuracy, respectively. Subsequently, we developed a cross platform video game for use with this device and tested using the Apple iPad. RESULTS The add-on device featured sensors mounted on the back of the incentive spirometer with an adjoining base containing electronics. Volume and flow piston locations were captured at 99% accuracy (+/- 1.4% SD) for volume and 100% accuracy for flow, while the breath algorithm displayed a 100% sensitivity and a 99% specificity. A custom video game operated in real time with Apple iPad devices. CONCLUSIONS Incentive spirometers are routinely used in hospitals, especially the postoperative period, but use recommendations lack thorough investigation partially due to a lack of data. We created and tested an effective and reusable add-on device for the incentive spirometer, allowing collection of previously inaccessible incentive spirometer data and demonstrating internet-of-things utility on a common hospital device. Spirometer data can facilitate research to improve adoption, guide clinical care, and incentivize adherence, to further investigate the clinical effectiveness of the incentive spirometer.

In order to study the impact of anesthesia opioid-related outcomes and acute and chronic post-surgical pain, we organized a multi-center study that comprehensively combined detailed perioperative data elements from multiple institutions. By combining pre- and post-operative patient reported outcomes with automatically extracted high resolution intra-operative data obtained through the Multicenter Perioperative Outcomes Group (MPOG), the authors sought to describe the impact of patient characteristics, pre-operative psychological factors, surgical procedure, anesthetic course, post-operative pain management, and post-discharge pain management on post-discharge pain profiles and opioid consumption patterns. This study is unique in that it utilized multi-center prospective data collection using a digital case report form integrated with the MPOG framework and database. Therefore, the study serves as a model for future studies using this innovative method. Full results will be reported in future papers; the purpose of this paper is to describe the methods of this study.

Background Opioids remain the primary mode of analgesia intraoperatively. There are limited data on how patient, procedural, and institutional characteristics influence intraoperative opioid administration. The aim of this retrospective, longitudinal study from 2012 to 2016 was to assess how intraoperative opioid dosing varies by patient and clinical care factors and across multiple institutions over time. Methods Demographic, surgical procedural, anesthetic technique, and intraoperative analgesia data as putative variables of intraoperative opioid utilization were collected from 10 institutions. Log parenteral morphine equivalents (PME) was modeled in a multivariable linear regression model as a function of 15 covariates: 3 continuous covariates (age, anesthesia duration, year) and 12 factor covariates (peripheral block, neuraxial block, general anesthesia, emergency status, race, sex, remifentanil infusion, major surgery, American Society of Anesthesiologists [ASA] physical status, non-opioid analgesic count, Multicenter Perioperative Outcomes Group [MPOG] institution, surgery category). One interaction (year by MPOG institution) was included in the model. The regression model adjusted simultaneously for all included variables. Comparison of levels within a factor were reported as a ratio of medians with 95% credible intervals (CrI). Results A total of 1,104,324 cases between January 2012 and December 2016 were analyzed. The median (interquartile range) PME and standardized by weight PME per case for the study period were 15 (10-28) mg and 200 (111-347) μg/kg, respectively. As estimated in the multivariable model, there was a sustained decrease in opioid use (mean, 95% CrI) dropping from 152 (151-153) μg/kg in 2012 to 129 (129-130) μg/kg in 2016. The percent of variability in PME due to institution was 25.6% (24.8%-26.5%). Less opioids were prescribed in men (130 [129-130] μg/kg) than women (144 [143-145] μg/kg). The men to women PME ratio was 0.90 (0.89-0.90). There was substantial variability in PME administration among institutions, with the lowest being 80 (79-81) μg/kg and the highest being 186 (184-187) μg/kg; this is a PME ratio of 0.43 (0.42-0.43). Conclusions We observed a reduction in intraoperative opioid administration over time, with variability in dose ranging between sexes and by procedure type. Furthermore, there was substantial variability in opioid use between institutions even when adjusting for multiple variables.

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Perioperative controlled substance diversion and tracking have received increased regulatory focus throughout the United States. The authors’ institution developed and implemented an automated web-based software application for perioperative controlled substance management. The authors hypothesized that implementation of such a system reduces errors as measured by missing controlled substance medications, missing controlled substance kits (a package of multiple controlled substance medications), and missing witness signatures during kit return. Methods From December 1, 2014 to March 31, 2017, the authors obtained missing controlled substance medication, controlled substance kit, and witness return signature data during the preimplementation, implementation, and study period of the controlled substance management application at a single university hospital. This before and after study was based on a QI project at the authors’ institution. The authors included all cases requiring anesthesia services. The primary outcome of this study was the rate of missing controlled substance medications. Secondary outcomes included rates for kits not returned to pharmacy and missing kit return witness signatures. Results There were 54,302 cases during the preimplementation period, 57,670 cases during the implementation period, and 65,911 cases during the study period. The number of missing controlled substance medication (difference 0.7 per 1,000 cases; 95% CI, 0.38–1.02; P < 0.001) and kit return errors (difference 0.45 per 1,000 cases; 95% CI, 0.24–0.66, P < 0.001) declined after implementation of the application. There was no difference in the number of missing witness return signatures (difference 0.09 per 1,000 cases; 95% CI, −0.08 to 0.26, P = 0.350). A user survey with 206 of 485 (42%) response rate demonstrated that providers believed the new application managed controlled substances better than the previous system. Conclusions A software application that tracks perioperative controlled substance kits with deep integration into the electronic health record and pharmacy systems is associated with a decrease in management errors.