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In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay.A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay.A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P.05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P.05).In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

UNLABELLED: In this unblinded randomized control trial we compared electronic self-administered Pre-Admission Adult Anesthetic Questionnaires (PAAQ) using touchscreen technology with pen and paper. Patients were recruited in the Pre-assessment Clinic if they had completed a PAAQ in the surgeon's office. Patients were randomized to study PAAQ using paper, hand-held computer (PDA), touchscreen desktop computer (kiosk), or tablet. Patients also completed a preference and satisfaction survey. The main outcome measures were percent agreement between the prestudy and study PAAQ and time to completion. Only six of the 366 patients approached refused to participate. The median time to completion of the PAAQ was shortest on the kiosk (2.3 min) and longest on the PDA (3.2 min) (chi2 = 14.5; P = 0.002). The mean agreement between the prestudy and the study PAAQ was approximately 94% across all study arms. The proportion of participants expressing comfort before and after completing the PAAQ increased from 10% to 97% on the computerized arms and from 60% to 64% on the paper arm. Touchscreen computer technology is an accurate, efficient platform for patient-administered PAAQ. Patients expressed comfort using the technology and preference for computerized versus paper for future questionnaires. IMPLICATIONS: Self-administered electronic health questionnaires using touchscreen computer technology are an accurate means of collecting patient information in the preoperative setting and can provide a valuable basis for an electronic perioperative patient record. Patients expressed comfort and satisfaction with this method of questionnaire completion.

Introduction Hospital patients require physiological monitoring throughout their stay. Monitoring requirements depend on the hospital unit (e.g. Admission, OR, ICU, ward). Currently, monitoring devices are stationary and are connected by wires to sensors and patient. This is cumbersome for both patient and health care providers, and sensors must be disconnected when the patient is prepared for transfer between units. Further, sensors located in one unit are often incompatible with those in another.We propose a novel concept that simplifies patient monitoring throughout the hospital. Method Approach:We propose a two level wireless network (Fig. 1). A personal area network (PAN) is private to the patient and is responsible for the control of data communication. The PAN host device connects to all required sensors using a wide range of supported protocols (e.g. serial, USB,WiFi and Bluetooth), and is attached to the patient during the entire hospital stay. The PAN host then wirelessly transmits the standardized data to a local area network (LAN) that records patient health information in a database. This information can be retrieved in real time by either stationary monitoring devices or mobile devices of health care providers throughout the hospital network. Prototype: The prototype consists of two pulse oximeters (Nonin, USA) connected via Bluetooth and wired connection, respectively, to a computer with a Linux operating system that acts as the host for the PAN. The LAN consists of a server running a web-based sensor actuator network portal called Sense Tecnic [1]. AWiFi enabled mobile device is used as the monitoring display. Results \& Discussion Blood oxygen saturation and heart rate trend signals are recorded and displayed in real time at a 1 Hz update rate. The web-based data portal allows platform independent, real-time monitoring. The PAN allows for easy connection of sensors to the patient and facilitates monitoring during patient movement and transportation. This approach will facilitate the use of elementary sensors without interruption throughout the hospital. Unit specific sensors can be added to the PAN when required. Future work will include geolocation by indoor triangulation using theWiFi network, and size reduction of the PAN host.