scholarly journals Spatial Action Maps Augmented with Visit Frequency Maps for Exploration Tasks

2021 ◽  
Author(s):  
Zixing Wang ◽  
Nikolaos Papanikolopoulos
Keyword(s):  
Visit Frequency

1273-P: Outpatient Diabetes Encounter Visit Frequency in the T1D Exchange Quality Improvement Collaborative

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1273-P
Author(s):  
GUY T. ALONSO ◽  
SARAH THOMAS ◽  
COLLEEN GAREY ◽  
DON A. BUCKINGHAM ◽  
ALYSSA B. CABRERA ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Quality Improvement Collaborative ◽  
Visit Frequency

scholarly journals Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients

2020 ◽  
Vol 41 (S1) ◽  
pp. s12-s12
Author(s):  
D. M. Hasibul Hasan ◽  
Philip Polgreen ◽  
Alberto Segre ◽  
Jacob Simmering ◽  
Sriram Pemmaraju
Keyword(s):  
Time Of Day ◽  
Microsoft Kinect ◽  
Critical Parameters ◽  
Model Parameters ◽  
Patient Room ◽  
University Of Iowa ◽  
Velocity Change ◽  
Fine Grained ◽  
Visit Frequency ◽  
Contact Patterns

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.Funding: NoneDisclosures: None

scholarly journals Planning the Episode: Home Care Admission Nurse Decision-Making Regarding the Patient Visit Pattern

2021 ◽  
pp. 108482232199077
Author(s):  
Paulina S. Sockolow ◽  
Kathryn H. Bowles ◽  
Carl Pankok ◽  
Yingjie Zhou ◽  
Sheryl Potashnik ◽  
...  
Keyword(s):  
Decision Making ◽  
Home Health Care ◽  
Clinical Decision ◽  
Structured Interviews ◽  
Skilled Nursing ◽  
Patient Interaction ◽  
Visit Frequency ◽  
Nursing Visit ◽  
Structured Information ◽  
Episode Duration

During home health care (HHC) admissions, nurses provide input into decisions regarding the skilled nursing visit frequency and episode duration. This important clinical decision can impact patient outcomes including hospitalization. Episode duration has recently gained greater importance due to the Centers for Medicare and Medicaid Services (CMS) decrease in reimbursable episode length from 60 to 30 days. We examined admissions nurses’ visit pattern decision-making and whether it is influenced by documentation available before and during the first home visit, agency standards, other disciplines being scheduled, and electronic health record (EHR) use. This observational mixed-methods study included admission document analysis, structured interviews, and a think-aloud protocol with 18 nurses from 3 diverse HHC agencies (6 at each) admitting 2 patients each (36 patients). Findings show that prior to entering the home, nurses had an information deficit; they either did not predict the patient’s visit frequency and episode duration or stated them based on experience with similar patients. Following patient interaction in the home, nurses were able to make this decision. Completion of documentation using the EHR did not appear to influence visit pattern decisions. Patient condition and insurance restrictions were influential on both frequency and duration. Given the information deficit at admission, and the delay in visit pattern decision making, we offer health information technology recommendations on electronic communication of structured information, and EHR documentation and decision support.

scholarly journals How Perceived Sensory Dimensions of Forest Park Are Associated with Stress Restoration in Beijing?

2022 ◽  
Vol 19 (2) ◽  
pp. 883
Author(s):  
Congying An ◽  
Jinglan Liu ◽  
Qiaohui Liu ◽  
Yuqi Liu ◽  
Xiaoli Fan ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Individual Characteristics ◽  
Forest Park ◽  
Positive Effects ◽  
Activity Intensity ◽  
Visit Frequency ◽  
Significant Difference ◽  
Visit Duration ◽  
Forest Parks

A growing number of studies suggest that the perceived sensory dimensions (PSDs) of green space are associated with stress restoration offered by restorative environment. However, there is little known about PSDs and stress restoration as well as their relationship to forest park. To fill this gap, an on-site questionnaire survey was conducted in three forest parks in Beijing, as a result of which a total number of 432 completed responses were collected and analyzed. The mean values of PSDs were used to represent PSDs of forest park. Using independent sample t-test and ANOVA, this study analyzed the individual characteristics that affected PSDs and stress restoration. Linear mixed model was used to identify the relationship between PSDs and stress restoration of forest park, which took into account the interactions of stress level and PSDs. The results showed that: (1) the perceived degree of PSDs in forest park from strong to weak was Serene, Space, Nature, Rich in species, Prospect, Refuge, Social and Culture, which varied with visitors’ gender, age, level of stress, visit frequency, activity intensity, visit duration and commuting time; (2) in PSDs, Refuge, Serene, Social and Prospect had significantly positive effects on the stress restoration of forest parks (3) there was no significant difference in the effect of the eight PSDs on the stress restoration between different stress groups; (4) stress restoration was influenced by visitors’ gender, age, visit frequency and visit duration. These findings can offer references for managers to improve the health benefits of forest park for visitors, and can enrich the knowledge about PSDs and stress restoration.

scholarly journals Community-based Guinea worm surveillance in Chad: Evaluating a system at the intersection of human and animal disease

2021 ◽  
Vol 15 (3) ◽  
pp. e0009285
Author(s):  
Beth L. Rubenstein ◽  
Sharon L. Roy ◽  
Karmen Unterwegner ◽  
Sarah Yerian ◽  
Adam Weiss ◽  
...  
Keyword(s):  
Active Surveillance ◽  
Parasitic Infection ◽  
Care Seeking ◽  
Community Based ◽  
Knowledge Community ◽  
Logistic Regression Models ◽  
Guinea Worm ◽  
Visit Frequency ◽  
Community Volunteers ◽  
Changes Over Time

Background Guinea worm is a debilitating parasitic infection targeted for eradication. Annual human cases have dropped from approximately 3,500,000 in 1986 to 54 in 2019. Recent identification of canine cases in Chad threatens progress, and therefore detection, prevention, and containment of canine cases is a priority. We investigated associations between disease knowledge, community engagement, and canine cases in Chad to identify opportunities to improve active surveillance. Methods We surveyed 627 respondents (villagers, local leaders, community volunteers, and supervisors) across 45 villages under active surveillance. Descriptive statistics were analyzed by respondent category. Logistic regression models were fitted to assess the effects of volunteer visit frequency on villager knowledge. Results Knowledge increased with respondents’ associations with the Guinea worm program. Household visit frequency by community volunteers was uneven: 53.0% of villagers reported visits at least twice weekly and 21.4% of villagers reported never being visited. Villagers visited by a volunteer at least twice weekly had better knowledge of Guinea worm symptoms (OR: 1.71; 95% CI: 1.04–2.79) and could name more prevention strategies (OR: 2.04; 95% CI: 1.32–3.15) than villagers visited less frequently. The primary motivation to report was to facilitate care-seeking for people with Guinea worm. Knowledge of animal “containment” to prevent contamination of water, knowledge of rewards for reporting animal cases, and ability to name any reasons to report Guinea worm were each positively correlated with village canine case counts. Conclusions Community volunteers play crucial roles in educating their neighbors about Guinea worm and facilitating surveillance. Additional training and more attentive management of volunteers and supervisors could increase visit frequency and further amplify their impact. Emphasizing links between animal and human cases, the importance of animal containment, and animal rewards might improve surveillance and canine case detection. The surveillance system should be evaluated routinely to expand generalizability of data and monitor changes over time.

scholarly journals Implementing Health Related Social Needs Screening in an Outpatient Clinic: A Cross-Sectional Study

2021 ◽  
Author(s):  
Raman Nohria ◽  
Nan Xiao ◽  
Rubeen Guardado ◽  
Mari-Lynn Drainoni ◽  
Cara Smith ◽  
...  
Keyword(s):  
Random Sample ◽  
Disease Burden ◽  
Implementation Strategies ◽  
Clinic Visit ◽  
Social Needs ◽  
Comorbid Disease ◽  
Cross Sectional ◽  
Visit Frequency ◽  
Clinic Site ◽  
Health Related

Abstract BackgroundTo date, health-related social needs (HRSN) screening implementation efforts have reported high rates of identified social needs. However, little is known about how screening processes may fail to leverage implementation strategies to optimize reach, and thereby unintentionally target non-representative groups for screening and referral programs. MethodsElectronic medical data were abstracted from 2016-2020 for 4,731 screened patients from 7 affiliated clinics of a federally-qualified health center (FQHC). Unscreened patients were pulled first as a random sample from the study period, then matched based on clinic site and clinic visit frequency. Sociodemographic traits, comorbid conditions, and outpatient encounter type and frequency were compared between the screened and unscreened patient cohorts using descriptive statistics.ResultsScreened patients (n-4731) had a median of 3.3 (+/- 2.5) unmet HRSN. Compared to a random sample of unscreened FQHC patients, screened patients had significantly more clinic visits (26.8 vs 16.3; p<0.05) and carried a higher comorbid disease burden (3+ conditions: 8% vs 2%; p<0.05). When the unscreened cohort was matched to the screened cohort for clinic site, these findings remained constant. Due to high visit frequency in the screened cohort, our analysis was matched for clinic visit frequency. Screened patients continued to demonstrate a higher comorbid disease burden (3+ conditions, 8% vs 3%; p<0.05), but only had a higher prevalence of 4 chronic conditions (diabetes, hypertension, chronic kidney disease, and anxiety/depression). ConclusionsWithout an a priori plan for implementation, we have found a predisposition to screen patients who visit outpatient services more often and have a higher comorbid disease burden. HRSN screening processes will benefit from implementation strategies to improve reach and to ensure maximal uptake of screening.

Abstract 16910: High-Frequency In-Person Visits During Clinical Trial Enrollment is Associated With Relative Reduction in Event Rates in Heart Failure Patients Followed Longitudinally

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Liana C Brooks ◽  
Rohan R Bhat ◽  
Robyn F Farrell ◽  
Mark W Schoenike ◽  
John A Sbarbaro ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
High Frequency ◽  
Trial Participation ◽  
Trial Period ◽  
Single Center ◽  
Hospitalization Rates ◽  
Visit Frequency ◽  
Clinical Trial Enrollment ◽  
Event Rates

Introduction: The COVID-19 Pandemic has mandated limiting routine visit frequency for patients with chronic cardiovascular (CV) diseases. In patients with heart failure (HF) followed longitudinally, the period of clinical trial participation provides an opportunity to evaluate the influence of high-frequency per-protocol in-person visits compared to less frequent routine visits during longitudinal clinical care. Hypothesis: Patients enrolled in clinical trials will have a lower CV and HF event rates during periods of trial enrollment than during non-trial periods. Methods: We examined clinical characteristics, CV and HF hospitalization rates, and outcomes in patients with HF receiving longitudinal HF care at a single center. We evaluated hospitalization rates during the 1-year preceding trial enrollment and hospitalization and death rates during enrollment in clinical trials and for up to 1 year following trial completion. Results: Among the 121 patients enrolled in HF clinical trials, 72% were HFrEF (age 62±11, 19% females, BMI 30.4±6.0, LVEF 25±7, NYHA 2.7±0.6, NT-proBNP 2336±2671) and 28% were HFpEF (age 69±9, BMI 32.1±5.5, 29% females, LVEF 60±10, NYHA 2.4±0.5, NT-proBNP 957±997). Average clinical trial exposure was 8±6.6 months. Per-protocol visit frequency was 16±7 per year during clinical trial enrollment. In the one-year pre-trial period, compared to the within-trial period, CV hospitalizations were 0.88/patient-year vs. 0.32/patient-year (p<0.001) and HF hospitalizations were 0.63/patient-year and 0.24/patient-year (p<0.001), with a mortality rate of 0.04/patient-year during trial participation. In the period of up-to 1 year following the end of trial enrollment CV and HF hospitalizations were intermediate at 0.51/patient-year and 0.27/patient-year with an annualized incremental mortality rate of 0.03/patient-year. Conclusion: In HF patients followed longitudinally at a single center, periods of clinical trial enrollment were associated with high visit frequency and lower CV and HF hospitalization rates. These findings highlight the potential benefits of trial enrollment and high-frequency visits for HF patients at a time when routine visit frequency is being carefully considered during the COVID-19 Pandemic.

PSXIV-9 Predicting dry matter intake in beef cattle with water intake and other variables

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 478-478
Author(s):  
Matthew E Wilson ◽  
Kevin Shaffer ◽  
Shane Plum ◽  
Kevin Garossino ◽  
Nicky Lansink ◽  
...  
Keyword(s):  
Body Weight ◽  
Water Intake ◽  
Dry Matter ◽  
Predictive Accuracy ◽  
Dry Matter Intake ◽  
Animal Performance ◽  
Performance Variables ◽  
Visit Frequency ◽  
Study Results ◽  
Full Body

Abstract Technology that facilitates estimation of individual animal intake rates in group-housed settings will result in improvements in animal production and management efficiency. Estimating intake in pasture settings may benefit from models that use other variables as proxies. Relationships among dry matter intake (DMI), animal performance variables, and environmental variables to model DMI were investigated. 202 animals were studied in a drylot setting (153 bulls for 85 days and 55 steers for 55 days) using VYTELLE SENSETM In-Pen-Weighing and Feed-Intake nodes. A machine learning model was calibrated using: DMI, sex, age, full body weight, ADG, water intake, water visit frequency and duration. DMI was positively related to full body weight (r = 0.39, P &lt; 0.001), water intake (r=0.23, P &lt; 0.001), and ADG (r=0.18, P &lt; 0.001). In addition, DMI had significant but weak correlations with water visit frequency (r=0.031, P &lt; 0.001). DMI exhibited weak negative relationships with maximum air temperature (r=-0.094, P &lt; 0.001) maximum relative humidity (r=-0.056, P &lt; 0.001), net radiation (r=-0.040, P &lt; 0.001), and precipitation (r=-0.022, P &lt; 0.001). Weak positive relationships were observed between DMI and maximum wind speed (r=0.031, P &lt; 0.001) and direction (r=-0.022, P &lt; 0.001). The model was validated with resultant average RMSE of 1.06 kg for daily predicted DMI compared to measured daily DMI. In addition, when daily predicted DMI was averaged for each animal, the accuracy of model results improved with RMSE of 0.11 kg. Study results demonstrate that inclusion of water intake and animal performance variables improves predictive accuracy of DMI. Validating and refining the model used to predict DMI in drylots will facilitate future extrapolation to larger group field settings. Vytelle and its logo are trademarks of Vytelle, LLC.

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