Comparing multiple infection control measures in a nursing home setting: a simulation study.

OBJECTIVE: Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks. DESIGN: Agent-based simulation study. SETTING: Simulation environment of a small nursing home. METHODS: We collected temporally detailed and spatially fine-grained location information from nursing home healthcare workers (HCWs) using sensor motes. We used these data to power an agent-based simulation of a COVID-19 outbreak using realistic time-varying estimates of infectivity and diagnostic sensitivity. Under varying community prevalence and transmissibility, we compared the mitigating effects of (i) regular screening and isolation, (ii) inter-resident contact restrictions, (iii) reduced HCW presenteeism, and (iv) modified HCW scheduling. RESULTS: Across all configurations tested, screening every other day and isolating positive cases decreased the attack rate by an average of 27% to 0.501 on average, while contact restrictions decreased the attack rate by an average of 35%, resulting in an attack rate of only 0.240, approximately half that of screening/isolation. Combining both interventions impressively produced an attack rate of only 0.029. Halving the observed presenteeism rate led to an 18% decrease in the attack rate, but if combined with screening every 6 days, the effect of reducing presenteeism was negligible. Altering work schedules had negligible effects on the attack rate. CONCLUSIONS: Universal contact restrictions are highly effective for protecting vulnerable nursing home residents, yet adversely affect physical and mental health. In high transmission and/or high community prevalence situations, restricting inter-resident contact to groups of 4 was effective and made highly effective when paired with weekly testing.

Predicting use of a gait-stabilizing device using a Wii Balance Board.

Gait-stabilizing devices (GSDs) are effective at preventing falls, but people are often reluctant to use them until after experiencing a fall. Inexpensive, convenient, and effective methods for predicting which patients need GSDs could help improve adoption. The purpose of this study was to determine if a Wii Balance Board (WBB) can be used to determine whether or not patients use a GSD. We prospectively recruited participants ages 70-100, some who used GSDs and some who did not. Participants first answered questions from the Modified Vulnerable Elders Survey, and then completed a grip-strength test using a handgrip dynamometer. Finally, they were asked to complete a series of four 30-second balance tests on a WBB in random order: (1) eyes open, feet apart; (2) eyes open, feet together; (3) eyes closed, feet apart; and (4) eyes closed, feet together. The four-test series was repeated a second time in the same random order. The resulting data, represented as 25 features extracted from the questionnaires and the grip test, and data from the eight balance tests, were used to predict a subject's GSD use using generalized functional linear models based on the Bernoulli distribution. 268 participants were consented; 62 were missing data elements and were removed from analysis; 109 were not GSD users and 97 were GSD users. The use of velocity and acceleration information from the WBB improved upon predictions based solely on grip strength, demographic, and survey variables. The WBB is a convenient, inexpensive, and easy-to-use device that can be used to recommend whether or not patients should be using a GSD.

Comparison of Different Antibiotics and the Risk for Community-Associated Clostridioides difficile Infection: A Case-Control Study.

Background: Antibiotics are the greatest risk factor for Clostridioides difficile infection (CDI). Risk for CDI varies across antibiotic types and classes. Optimal prescribing and stewardship recommendations require comparisons of risk across antibiotics. However, many prior studies rely on aggregated antibiotic categories or are underpowered to detect significant differences across antibiotic types. Using a large database of real-world data, we evaluate community-associated CDI risk across individual antibiotic types. Methods: We conducted a matched case-control study using a large database of insurance claims capturing longitudinal health care encounters and medications. Case patients with community-associated CDI were matched to 5 control patients by age, sex, and enrollment period. Antibiotics prescribed within 30 days before the CDI diagnosis along with other risk factors, including comorbidities, health care exposures, and gastric acid suppression were considered. Conditional logistic regression and a Bayesian analysis were used to compare risk across individual antibiotics. A sensitivity analysis of antibiotic exposure windows between 30 and 180 days was conducted. Results: We identified 159 404 cases and 797 020 controls. Antibiotics with the greatest risk for CDI included clindamycin and later-generation cephalosporins, and those with the lowest risk included minocycline and doxycycline. We were able to differentiate and order individual antibiotics in terms of their relative level of associated risk for CDI. Risk estimates varied considerably with different exposure windows considered. Conclusions: We found wide variation in CDI risk within and between classes of antibiotics. These findings ordering the level of associated risk across antibiotics can help inform tradeoffs in antibiotic prescribing decisions and stewardship efforts.

Using Thermal Imaging to Track Cellulitis.

Background: Cellulitis is a common soft tissue infection and a major cause of morbidity. The diagnosis is based almost exclusively on clinical history and physical exam. To improve the diagnosis of cellulitis, we used a thermal camera to track how skin temperature of the affected area changed during a hospital stay for patients with cellulitis. Methods: We recruited 120 patients admitted with a diagnosis of cellulitis. Daily thermal images of the affected limb were taken. Temperature intensity and area were analyzed from the images. Highest daily body temperature and antibiotics administered were also collected.We estimated a longitudinal linear mixed-effects model with a random intercept for the affected body area. All observations on a given day were included, and we used an integer time indicator indexed to the initial day (ie, t = 1 for the first day the patient was observed, etc.). We then analyzed the effect of this time trend on both severity (ie, normalized temperature) and scale (ie, area of skin with elevated temperature). Results: We analyzed thermal images from the 41 patients with a confirmed case of cellulitis who had at least 3 days of photos. For each day that the patient was observed, the severity decreased by 1.63 (95% CI, -13.45 to 10.32) units on average, and the scale decreased by 0.63 (95% CI, -1.08 to -0.17) points on average. Also, patients' body temperatures decreased by 0.28°F each day (95% CI, -0.40 to -0.17). Conclusions: Thermal imaging could be used to help diagnose cellulitis and track clinical progress.

Risk for Asymptomatic Household Transmission of Clostridioides difficile Infection Associated with Recently Hospitalized Family Members.

We evaluated whether hospitalized patients without diagnosed Clostridioides difficile infection (CDI) increased the risk for CDI among their family members after discharge. We used 2001-2017 US insurance claims data to compare monthly CDI incidence between persons in households with and without a family member hospitalized in the previous 60 days. CDI incidence among insurance enrollees exposed to a recently hospitalized family member was 73% greater than enrollees not exposed, and incidence increased with length of hospitalization among family members. We identified a dose-response relationship between total days of within-household hospitalization and CDI incidence rate ratio. Compared with persons whose family members were hospitalized <1 day, the incidence rate ratio increased from 1.30 (95% CI 1.19-1.41) for 1-3 days of hospitalization to 2.45 (95% CI 1.66-3.60) for >30 days of hospitalization. Asymptomatic C. difficile carriers discharged from hospitals could be a major source of community-associated CDI cases.

COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit.

This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and-once the first symptomatic patient is detected-isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.

Gamifying accelerometer use increases physical activity levels of individuals pre-disposed to type II diabetes.

Physical activity is important for preventing obesity and diabetes, but most obese and pre-diabetic patients are not physically active. We developed a Fitbit-based game called MapTrek that promotes walking. We recruited obese and pre-diabetic patients. Half were randomly assigned to the control group and given a Fitbit alone. The others were given a Fitbit plus MapTrek. The MapTrek group participated in 6 months of weekly virtual races. Each week, participants were placed in a race with 9 others who achieved a similar number of steps in the previous week's race. Participants moved along the virtual route by the steps recorded on their Fitbit and received daily walking challenges via text message. Text messages also had links to the race map and leaderboard. We used a Bayesian mixed effects model to analyze the number of steps taken during the intervention. A total of 192 (89%) participants in the control group and 196 (91%) in the MapTrek group were included in the analyses. MapTrek significantly increased step counts when it began: MapTrek participants walked almost 1,700 steps more than the control group on the first day of the intervention. We estimate that there is a 97% probability that the effect of MapTrek is at least 1,000 additional steps per day throughout the course of the 6-month intervention and that MapTrek participants would have walked an additional 81 miles, on average, before the effect ended. Our MapTrek intervention led to significant extra walking by the MapTrek participants.

Multilevel mHealth Intervention Increases Physical Activity of Older Adults Living in Retirement Community.

BACKGROUND: Regular physical activity is crucial for healthy aging, but older adults are the least active age group. This study explored the feasibility, acceptability, and efficacy of a multilevel mHealth intervention for increasing physical activity of older adults living in a retirement community. METHODS: Participants included 54 older adults (mean age = 81.2 y, 77.8% female, 98.1% white) living in a retirement community. Participants received a Fitbit Zip and access to a multilevel mHealth physical activity intervention (MapTrek Residential) for 8 weeks. Physical activity (in steps per day) and intervention compliance (days worn) were measured objectively with the Fitbit for 12 weeks (8-wk intervention plus 4-wk follow-up). Psychosocial outcomes (social support, self-efficacy, and outcome expectations) were assessed at baseline and 8 weeks. Acceptability outcomes were assessed with an open-ended process evaluation survey and focus groups. Descriptive statistics and linear mixed models were used to examine intervention effects. RESULTS: Participants increased daily steps from 5438 steps per day at baseline (95% CI, 4620 to 6256) to 6201 steps per day (95% CI, 5359 to 7042) at week 8 (P < .0001) but this was not maintained at 12 weeks (P = .92). CONCLUSIONS: Our multilevel mHealth physical activity intervention was effective for increasing physical activity older adults over 8 weeks. Additional research focused on maintaining physical activity gains with this approach is warranted.

Risk for Clostridioides difficile Infection Among Hospitalized Patients Associated With Multiple Healthcare Exposures Prior to Admission.

BACKGROUND: Clostridioides difficile infection (CDI) is a common healthcare-associated infection and is often used as an indicator of hospital safety or quality. However, healthcare exposures occurring prior to hospitalization may increase risk for CDI. We conducted a case-control study comparing hospitalized patients with and without CDI to determine if healthcare exposures prior to hospitalization (ie, clinic visits, antibiotics, family members with CDI) were associated with increased risk for hospital-onset CDI, and how risk varied with time between exposure and hospitalization. METHODS: Records were collected from a large insurance-claims database from 2001 to 2017 for hospitalized adult patients. Prior healthcare exposures were identified using inpatient, outpatient, emergency department, and prescription drug claims; results were compared between various CDI case definitions. RESULTS: Hospitalized patients with CDI had significantly more frequent healthcare exposures prior to admission. Healthcare visits, antibiotic use, and family exposures were associated with greater likelihood of CDI during hospitalization. The degree of association diminished with time between exposure and hospitalization. Results were consistent across CDI case definitions. CONCLUSIONS: Many different prior healthcare exposures appear to increase risk for CDI presenting during hospitalization. Moreover, patients with CDI typically have multiple exposures prior to admission, confounding the ability to attribute cases to a particular stay.

Diabetic Foot Surveillance Using Mobile Phones and Automated Software Messaging, a Randomized Observational Trial.

Background: Early detection of diabetic foot ulcers can improve outcomes. However, patients do not always monitor their feet or seek medical attention when ulcers worsen. New approaches for diabetic-foot surveillance are needed. The goal of this study was to determine if patients would be willing and able to regularly photograph their feet; evaluate different foot-imaging approaches; and determine clinical adequacy of the resulting pictures. Methods: We recruited adults with diabetes and assigned them to Self Photo (SP), Assistive Device (AD), or Other Party (OP) groups. The SP group photographed their own feet, while the AD group used a selfie stick; the OP group required another adult to photograph the patient's foot. For 8 weeks, we texted all patients requesting that they text us a photo of each foot. The collected images were evaluated for clinical adequacy. Numbers of (i) submitted and (ii) clinically useful images were compared among groups using generalized linear models and generalized linear mixed models. Results: A total of 96 patients consented and 88 participated. There were 30 patients in SP, 29 in AD, and 29 in OP. The completion rate was 77%, with no significant differences among groups. However, 74.1% of photographs in SC, 83.7% in AD, 92.6% in OP were determined to be clinically adequate, and these differed statistically significantly. Conclusions: Patients with diabetes are willing and able to take photographs of their feet, but using selfie sticks or having another adult take the photographs increases the clinical adequacy of the photographs.Level of Evidence: II.

Association of Household Exposure to Primary Clostridioides difficile Infection With Secondary Infection in Family Members.

Importance: Clostridioides difficile infection (CDI) is a common hospital-acquired infection. Whether family members are more likely to experience a CDI following CDI in another separate family member remains to be studied. Objective: To determine the incidence of potential family transmission of CDI. Design, Setting, and Participants: In this case-control study comparing the incidence of CDI among individuals with prior exposure to a family member with CDI to those without prior family exposure, individuals were binned into monthly enrollment strata based on exposure status (eg, family exposure) and confounding factors (eg, age, prior antibiotic use). Data were derived from population-based, longitudinal commercial insurance claims from the Truven Marketscan Commercial Claims and Encounters and Medicare Supplemental databases from 2001 to 2017. Households with at least 2 family members continuously enrolled for at least 1 month were eligible. CDI incidence was computed within each stratum. A regression model was used to compare incidence of CDI while controlling for possible confounding characteristics. Exposures: Index CDI cases were identified using inpatient and outpatient diagnosis codes. Exposure risks 60 days prior to infection included CDI diagnosed in another family member, prior hospitalization, and antibiotic use. Main Outcomes and Measures: The primary outcome was the incidence of CDI in a given monthly enrollment stratum. Separate analyses were considered for CDI diagnosed in outpatient or hospital settings. Results: A total of 224 818 cases of CDI, representing 194 424 enrollees (55.9% female; mean [SD] age, 52.8 [22.2] years) occurred in families with at least 2 enrollees. Of these, 1074 CDI events (4.8%) occurred following CDI diagnosis in a separate family member. Prior family exposure was significantly associated with increased incidence of CDI, with an incidence rate ratio (IRR) of 12.47 (95% CI, 8.86-16.97); this prior family exposure represented the factor with the second highest IRR behind hospital exposure (IRR, 16.18 [95% CI, 15.31-17.10]). For community-onset CDI cases without prior hospitalization, the IRR for family exposure was 21.74 (95% CI, 15.12-30.01). Age (IRR, 9.90 [95% CI, 8.92-10.98] for ages ≥65 years compared with ages 0-17 years), antibiotic use (IRR, 3.73 [95% CI, 3.41-4.08] for low-risk and 14.26 [95% CI, 13.27-15.31] for high-risk antibiotics compared with no antibiotics), and female sex (IRR, 1.44 [95% CI, 1.36-1.53]) were also positively associated with incidence. Conclusions and Relevance: This study found that individuals with family exposure may be at significantly greater risk for acquiring CDI, which highlights the importance of the shared environment in the transmission and acquisition of C difficile.

A texting-based blood pressure surveillance intervention.

The authors examined whether using home BP measurements collected via a custom-built bi-directional-texting platform incorporated into patients' electronic medical records would lead to treatment calibration and improved BP management. Patients were randomized to either the intervention group and collected home measurements based on reminders and reported via bi-directional texting, or to the control group, with home BP measurement reporting via standard practice (eg, phone, electronic medical record portal) and instructed to return 7 morning and 7 evening BP measurements. Outcomes included number of BP measurements submitted, the number of medication changes, reduction in BP, and BP control. 72% of the intervention group submitted at least 14 readings, compared with 45% of the control group. BP control improved in both groups. However, the authors found no statistically significant difference in BP or the number of BP-medication changes at 1, 3, or 6 months compared with the control group.

Estimating the Attributable Disease Burden and Effects of Interhospital Patient Sharing on Clostridium difficile Infections.

OBJECTIVE: To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels. DESIGN: Retrospective observational study. METHODS: We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005-2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network. RESULTS: We found that 13% (95% confidence interval [CI], 7.6%-18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0-6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital. CONCLUSIONS: A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital's infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.

21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models.

This large-scale study, consisting of 21.3 million hand-hygiene opportunities from 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand-hygiene compliance. We examine the use of features such as temperature, relative humidity, influenza severity, day/night shift, federal holidays, and the presence of new medical residents in predicting daily hand-hygiene compliance; the investigation is undertaken using both a "global" model to glean general trends and facility-specific models to elicit facility-specific insights. The results suggest that colder temperatures and federal holidays have an adverse effect on hand-hygiene compliance rates, and that individual cultures and attitudes regarding hand hygiene exist among facilities.

Gamifying Accelerometer Use Increases Physical Activity Levels of Sedentary Office Workers.

BACKGROUND: Sedentary work is hazardous. Over 80% of all US jobs are predominantly sedentary, placing full-time office workers at increased risk for cardiovascular and metabolic morbidity and mortality. Thus, there is a critical need for effective workplace physical activity interventions. MapTrek is a mobile health platform that gamifies Fitbit use for the purpose of promoting physical activity. The purpose of this study was to test the efficacy of MapTrek for increasing daily steps and moderate-intensity steps over 10 weeks in a sample of sedentary office workers. METHODS AND RESULTS: Participants included 146 full-time sedentary office workers aged 21 to 65 who reported sitting at least 75% of their workday. Each participant received a Fitbit Zip to wear daily throughout the intervention. Participants were randomized to either a: (1) Fitbit-only group or 2) Fitbit + MapTrek group. Physical activity outcomes and intervention compliance were measured with the Fitbit activity monitor. The Fitbit + MapTrek group significantly increased daily steps (+2092 steps per day) and active minutes (+11.2 min/day) compared to the Fitbit-only arm, but, on average, participants' steps declined during the study period. CONCLUSIONS: MapTrek is an effective approach for increasing physical activity at a clinically meaningful level in sedentary office workers, but as with accelerometer use alone, the effect decreases over time. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03109535.

The effect of automated text messaging and goal setting on pedometer adherence and physical activity in patients with diabetes: A randomized controlled trial.

INTRODUCTION: Activity-monitoring devices may increase activity, but their effectiveness in sedentary, diseased, and less-motivated populations is unknown. METHODS: Subjects with diabetes or pre-diabetes were given a Fitbit and randomized into three groups: Fitbit only, Fitbit with reminders, and Fitbit with both reminders and goal setting. Subjects in the reminders group were sent text-message reminders to wear their Fitbit. The goal-setting group was sent a daily text message asking for a step goal. All subjects had three in-person visits (baseline, 3 and 6 months). We modelled daily steps and goal setting using linear mixed-effects models. RESULTS: 138 subjects participated with 48 in the Fitbit-only, 44 in the reminders, and 46 in the goal-setting groups. Daily steps decreased for all groups during the study. Average daily steps were 7123, 6906, and 6854 for the Fitbit-only, the goal-setting, and the reminders groups, respectively. The reminders group was 17.2 percentage points more likely to wear their Fitbit than the Fitbit-only group. Setting a goal was associated with a significant increase of 791 daily steps, but setting more goals did not lead to step increases. CONCLUSION: In a population of patients with diabetes or pre-diabetes, individualized reminders to wear their Fitbit and elicit personal step goals did not lead to increases in daily steps, although daily steps were higher on days when goals were set. Our intervention improved engagement and data collection, important goals for activity surveillance. This study demonstrates that new, more-effective interventions for increasing activity in patients with pre-diabetes and diabetes are needed.

Estimating Time Physicians and Other Health Care Workers Spend with Patients in an Intensive Care Unit Using a Sensor Network.

BACKGROUND: Time and motion studies have been used to investigate how much time various health care professionals spend with patients as opposed to performing other tasks. However, the majority of such studies are done in outpatient settings, and rely on surveys (which are subject to recall bias) or human observers (which are subject to observation bias). Our goal was to accurately measure the time physicians, nurses, and critical support staff in a medical intensive care unit spend in direct patient contact, using a novel method that does not rely on self-report or human observers. METHODS: We used a network of stationary and wearable mote-based sensors to electronically record location and contacts among health care workers and patients under their care in a 20-bed intensive care unit for a 10-day period covering both day and night shifts. Location and contact data were used to classify the type of task being performed by health care workers. RESULTS: For physicians, 14.73% (17.96%) of their time in the unit during the day shift (night shift) was spent in patient rooms, compared with 40.63% (30.09%) spent in the physician work room; the remaining 44.64% (51.95%) of their time was spent elsewhere. For nurses, 32.97% (32.85%) of their time on unit was spent in patient rooms, with an additional 11.34% (11.79%) spent just outside patient rooms. They spent 11.58% (13.16%) of their time at the nurses' station and 23.89% (24.34%) elsewhere in the unit. From a patient's perspective, we found that care times, defined as time with at least one health care worker of a designated type in their intensive care unit room, were distributed as follows: 13.11% (9.90%) with physicians, 86.14% (88.15%) with nurses, and 8.14% (7.52%) with critical support staff (eg, respiratory therapists, pharmacists). CONCLUSIONS: Physicians, nurses, and critical support staff spend very little of their time in direct patient contact in an intensive care unit setting, similar to reported observations in both outpatient and inpatient settings. Not surprisingly, nurses spend far more time with patients than physicians. Additionally, physicians spend more than twice as much time in the physician work room (where electronic medical record review and documentation occurs) than the time they spend with all of their patients combined.

Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms.

Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)-patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence. Methods. We used multiple Microsoft Kinects to track the 3-dimensional movement of HCWs and their hands within hospital rooms. We applied computer vision techniques to recognize and determine the position of fiducial markers attached to the patient's bed to determine the location of the HCW's hands with respect to the bed. To measure our system's ability to detect HCW-patient contacts, we counted each time a HCW's hands entered a virtual rectangular box aligned with a patient bed. To measure PPE adherence, we identified the hands, torso, and face of each HCW on room entry, determined the color of each body area, and compared it with the color of gloves, gowns, and face masks. We independently examined a ground truth video recording and compared it with our system's results. Results. Overall, for touch detection, the sensitivity was 99.7%, with a positive predictive value of 98.7%. For gowned entrances, sensitivity was 100.0% and specificity was 98.15%. For masked entrances, sensitivity was 100.0% and specificity was 98.75%; for gloved entrances, the sensitivity was 86.21% and specificity was 98.28%. Conclusions. Using computer vision and depth sensing, we can estimate potential HH opportunities at the bedside and also estimate adherence to PPE. Our fine-grained estimates of how and how often HCWs interact directly with patients can inform a wide range of patient-safety research.

Outpatient blood pressure monitoring using bi-directional text messaging.

To diagnose hypertension, multiple blood pressure (BP) measurements are recommended. We randomized patients into three groups: EMR-only (patients recorded BP measurements in an electronic medical record [EMR] web portal), EMR + reminders (patients were sent text message reminders to record their BP measurements in the EMR), and bi-directional text messaging (patients were sent a text message asking them to respond with their current BP). Subjects were asked to complete 14 measurements. Automated messages were sent to each patient in the bi-directional text messaging and EMR + reminder groups twice daily. Among 121 patients, those in the bi-directional text messaging group reported the full 14 measurements more often than both the EMR-only group (P < .001) and the EMR + reminders group (P = .038). Also, the EMR + reminders group outperformed the EMR-only group (P < .001). Bi-directional automated text messaging is an effective way to gather patient BP data. Text-message-based reminders alone are an effective way to encourage patients to record BP measurements.