Luis M. Ahumada

A Review of Analytics and Clinical Informatics in Health Care

Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. “Big Data”). Health care systems have leveraged Big Data for quality and performance improvements using analytics—the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.

Big data and visual analytics in anaesthesia and health care

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.

Association Between Ibuprofen Use and Severity of Surgically Managed Posttonsillectomy Hemorrhage.

Importance Ibuprofen used in postoperative management of pain after tonsillectomy has not been shown to increase the overall risk for posttonsillectomy hemorrhage (PTH). The severity of bleeding is difficult to quantify but may be a more important outcome to measure. Objective To evaluate the association between ibuprofen use and severity of PTH using transfusion events as a marker of severity. Design, Setting, and Participants This retrospective cohort study identified 8868 patients who underwent tonsillectomy from January 20, 2011, through June 30, 2014, at the tertiary academic Children’s Hospital of Philadelphia. Of these patients, 6710 met the inclusion criteria. Data were collected using electronic database acquisition and query. Main Outcomes and Measures Multivariate analysis was performed to identify independent prognostic factors for PTH and receipt of transfusion. Results Of the 6710 patients who met criteria for analysis (3454 male [51.5%] and 3256 female [48.5%]; median age, 5.4 years [interquartile range, 3.7-8.2 years]), 222 (3.3%) presented with PTH that required surgical control (sPTH). A total of 15 of the 8868 patients required transfusion for an overall risk for transfusion after tonsillectomy of 0.2%. Fifteen of 222 patients undergoing sPTH (6.8%) received transfusions. No significant independent increased risk for sPTH was associated with use of ibuprofen (adjusted odds ratio [OR], 0.90; 95% CI, 0.68-1.19). A significant independent association was found in the risk for sPTH in patients 12 years or older (adjusted OR, 2.74; 95% CI, 1.99-3.76) and in patients with a history of recurrent tonsillitis (adjusted OR, 1.52; 95% CI, 1.12-2.06). When using transfusion rates as a surrogate for severity of sPTH, transfusion increased by more than 3-fold among ibuprofen users compared with nonusers (adjusted OR, 3.16; 95% CI, 1.01-9.91), and the upper limit of the 95% CI suggests the difference could be nearly 10 times greater. Conclusions and Relevance The risk for sPTH is not increased with use of postoperative ibuprofen but is increased in patients 12 years or older and patients undergoing tonsillectomy with a history of recurrent tonsillitis. Hemorrhage severity is significantly increased with ibuprofen use when using transfusion rate as a surrogate marker for severity.

Visual analytical tool for evaluation of 10-year perioperative transfusion practice at a children's hospital

Children are a vulnerable population in the operating room, and are particularly at risk of complications from unanticipated hemorrhage. The decision to prepare blood products prior to surgery varies depending on the personal experience of the clinician caring for the patient. We present the first application of a data visualization technique to study large datasets in the context of blood product transfusions at a tertiary pediatric hospital. The visual analytical interface allows real-time interaction with datasets from 230 000 procedure records. Clinicians can use the visual analytical interface to analyze blood product usage based on procedure- and patient-specific factors, and then use that information to guide policies for ordering blood products.

Interactive pediatric emergency checklists to the palm of your hand - How the Pedi Crisis App traveled around the world

Cognitive aids help clinicians manage critical events and have been shown to improve outcomes by providing critical information at the point of care. Critical event guidelines, such as the Society of Pediatric Anesthesias Critical Events Checklists described in this article, can be distributed globally via interactive smartphone apps. From October 1, 2013 to January 1, 2014, we performed an observational study to determine the global distribution and utilization patterns of the Pedi Crisis cognitive aid app that the Society for Pediatric Anesthesia developed. We analyzed distribution and utilization metrics of individuals using Pedi Crisis on iOS (Apple Inc., Cupertino, CA) devices worldwide. We used Google Analytics software (Google Inc., Mountain View, CA) to monitor users’ app activity (eg, screen views, user sessions).

Perioperative Smartphone Apps and Devices for Patient-Centered Care

Smartphones have grown in ubiquity and computing power, and they play an ever-increasing role in patient-centered health care. The “medicalized smartphone” not only enables web-based access to patient health resources, but also can run patient-oriented software applications and be connected to health-related peripheral devices. A variety of patient-oriented smartphone apps and devices are available for use to facilitate patient-centered care throughout the continuum of perioperative care. Ongoing advances in smartphone technology and health care apps and devices should expand their utility for enhancing patient-centered care in the future.

The timing and prevalence of intraoperative hypotension in infants undergoing laparoscopic pyloromyotomy at a tertiary pediatric hospital.

Intraoperative hypotension may be associated with adverse outcomes in children undergoing surgery. Infants and neonates under 6 months of age have less autoregulatory cerebral reserve than older infants, yet little information exists regarding when and how often intraoperative hypotension occurs in infants.

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists’ reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine’s performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool’s sensitivity was 97.2% (95% CI 93–99.2%), specificity was 92.5% (95% CI 85.7–96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8–89.2) and specificity of 97.5% (95% CI 96.9–98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia

Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.

Safety Huddle Intervention for Reducing Physiologic Monitor Alarms: A Hybrid Effectiveness-Implementation Cluster Randomized Trial

BACKGROUND Monitor alarms occur frequently but rarely warrant intervention. OBJECTIVE This study aimed to determine if a safety huddle-based intervention reduces unit-level alarm rates or alarm rates of individual patients whose alarms are discussed, as well as evaluate implementation outcomes. DESIGN Unit-level, cluster randomized, hybrid effectiveness-implementation trial with a secondary patient-level analysis. SETTING Children’s hospital. PATIENTS Unit-level: all patients hospitalized on four control (n = 4177) and four intervention (n = 7131) units between June 15, 2015 and May 8, 2016. Patient-level: 425 patients on randomly selected dates postimplementation. INTERVENTION Structured safety huddle review of alarm data from the patients on each unit with the most alarms, with a discussion of ways to reduce alarms. MEASUREMENTS Unit-level: change in unit-level alarm rates between baseline and postimplementation periods in intervention versus control units. Patient-level: change in individual patients’ alarm rates between the 24 hours leading up to huddles and the 24 hours after huddles in patients who were discussed versus not discussed in huddles. RESULTS Alarm data informed 580 huddle discussions. In unit-level analysis, intervention units had 2 fewer alarms/patient-day (95% CI: 7 fewer to 6 more, P = .50) compared with control units. In patient-level analysis, patients discussed in huddles had 97 fewer alarms/patient-day (95% CI: 52–138 fewer, P < .001) in the posthuddle period compared with patients not discussed in huddles. Implementation outcome analysis revealed a low intervention dose of 0.85 patients/unit/day. CONCLUSIONS Safety huddle-based alarm discussions did not influence unit-level alarm rates due to low intervention dose but were effective in reducing alarms for individual children.