scholarly journals Preventing Non-attendance in Outpatient Appointments: Predictive Model Development, Validation, and Clinical Assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
High Risk ◽  
Model Development ◽  
Intervention Group ◽  
Resource Planning ◽  
Quality Of Healthcare ◽  
Phone Call ◽  
Outpatient Services ◽  
Healthcare Provision ◽  
Predictive Capacity ◽  
Outpatient Appointments

Abstract Background: Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.Methods: Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. Patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.Results: Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Average results for specificity and balanced accuracy for the prediction of non-attendance were 79.90% and 73.49% for dermatology, and 71.38% and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.Conclusions: The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

scholarly journals Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Model Development ◽  
Phone Call ◽  
Attendance Rate ◽  
Machine Learning Model ◽  
Tree Models ◽  
Outpatient Appointments

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

scholarly journals Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

2021 ◽  
Author(s):  
Damià Valero-Bover ◽  
Pedro González ◽  
Gerard Carot-Sans ◽  
Isaac Cano ◽  
Pilar Saura ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Model Development ◽  
Phone Call ◽  
Attendance Rate ◽  
Machine Learning Model ◽  
Tree Models ◽  
Outpatient Appointments

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

scholarly journals 4494 Predictors of Reintubation After Cardiac Surgery

2020 ◽  
Vol 4 (s1) ◽  
pp. 50-50
Author(s):  
Robert Edward Freundlich ◽  
Gen Li ◽  
Jonathan P Wanderer ◽  
Frederic T Billings ◽  
Henry Domenico ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Cardiac Surgery ◽  
High Risk ◽  
Query Language ◽  
Risk Estimation ◽  
Clinical Care ◽  
Model Development ◽  
Calibration Plot ◽  
Iterative Model ◽  
Strong Candidate

OBJECTIVES/GOALS: We modeled risk of reintubation within 48 hours of cardiac surgery using variables available in the electronic health record (EHR). This model will guide recruitment for a prospective, pragmatic clinical trial entirely embedded within the EHR among those at high risk of reintubation. METHODS/STUDY POPULATION: All adult patients admitted to the cardiac intensive care unit following cardiac surgery involving thoracotomy or sternotomy were eligible for inclusion. Data were obtained from operational and analytical databases integrated into the Epic EHR, as well as institutional and departmental-derived data warehouses, using structured query language. Variables were screened for inclusion in the model based on clinical relevance, availability in the EHR as structured data, and likelihood of timely documentation during routine clinical care, in the hopes of obtaining a maximally-pragmatic model. RESULTS/ANTICIPATED RESULTS: A total of 2325 patients met inclusion criteria between November 2, 2017 and November 2, 2019. Of these patients, 68.4% were male. Median age was 63.0. The primary outcome of reintubation occurred in 112/2325 (4.8%) of patients within 48 hours and 177/2325 (7.6%) at any point in the subsequent hospital encounter. Univariate screening and iterative model development revealed numerous strong candidate predictors (ANOVA plot, figure 1), resulting in a model with acceptable calibration (calibration plot, figure 2), c = 0.666. DISCUSSION/SIGNIFICANCE OF IMPACT: Reintubation is common after cardiac surgery. Risk factors are available in the EHR. We are integrating this model into the EHR to support real-time risk estimation and to recruit and randomize high-risk patients into a clinical trial comparing post-extubation high flow nasal cannula with usual care. CONFLICT OF INTEREST DESCRIPTION: REF has received grant funding and consulting fees from Medtronic for research on inpatient monitoring.

Abstract T MP103: Effect of Follow-up Phone Calls on 30-day Readmission Rates for Stroke Patients

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Kelly Anderson
Keyword(s):  
Intervention Group ◽  
Signs And Symptoms ◽  
Lifestyle Changes ◽  
Phone Call ◽  
Control Group ◽  
Readmission Rates ◽  
Convenience Sample ◽  
Phone Calls ◽  
Number Of Patients

Background and Purpose: Patients who are hospitalized for a stroke or TIA go home with a great deal of information about risk factors, medications, diet and exercise, signs and symptoms of stroke and follow-up care. This information may be difficult for the patient or caregiver to understand and can be overwhelming in the face of a new life-changing event. In addition, The Centers for Medicare and Medicaid Services will start publicly reporting 30-day readmission rates beginning in 2016. The purpose of this study is to determine if follow-up phone calls with a nurse help to reduce 30 day readmission rates for patients with stroke and TIA. Methods: This study utilized a convenience sample of adult patients who were admitted for ischemic stroke, ICH, SAH or TIA from March 2013 to February 2014. Patients in the intervention group participated in a phone call seven days after discharge to assess their compliance with medications, physician appointments and lifestyle changes. The proportion of readmissions between the groups was compared with Fisher’s exact test. Results: The total number of patients enrolled in the study was 586 and there were no significant differences in demographics between the control and intervention groups. Of the 533 patients in the control group, 54 (10%) of them were readmitted, including 11 patients readmitted for elective surgical procedures. Of the 52 patients in the intervention group, 3 (5.7%) of them were readmitted before the 7-day phone call. Of the 49 patients who participated in the 7-day phone call, none of them were readmitted ( p =0.0098). Conclusions: Patients who participate in a 7-day phone call appear to benefit and are less likely to be readmitted to the hospital. Other strategies may need to be considered for patients who are at higher risk, and thus more likely to be readmitted within seven days of discharge. In addition, some providers may wish to reconsider how they schedule elective procedures for secondary stroke prevention.

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

INOVASIA Study: A Randomized Open Controlled Trial to Evaluate Pravastatin to Prevent Preeclampsia and its Effects on sFlt1/PLGF Levels

2021 ◽  
Author(s):  
Muhammad Ilham Aldika Akbar ◽  
Angelina Yosediputra ◽  
Raditya Eri Pratama ◽  
Nur Lailatul Fadhilah ◽  
Sulistyowati Sulistyowati ◽  
...  
Keyword(s):  
High Risk ◽  
Pregnant Women ◽  
Controlled Trial ◽  
Intervention Group ◽  
Perinatal Outcomes ◽  
Neonatal Morbidity ◽  
Clinical Risk Factor ◽  
Control Group ◽  
Significant Deterioration ◽  
Plgf Ratio

Objectives To evaluate the effect of pravastatin to prevent preeclampsia (PE) in pregnant women at a high risk of developing preeclampsia and the maternal and perinatal outcomes and the sFlt1/PLGF ratio. Study Design This is an open labelled RCT part of INOVASIA trial. Pregnant women at a high risk of developing PE were recruited and randomized into an intervention group (40) and a control group (40). The inclusion criteria consisted of pregnant women with positive clinical risk factor and abnormal uterine artery doppler examination at 10-20 weeks gestational age. The control group received low dose aspirin (80 mg/day) and calcium (1 g/day), while the intervention group received additional pravastatin (20 mg twice daily) starting from 14-20 weeks gestation until delivery. Research blood samples were collected before the first dose of pravastatin and before delivery. The main outcome was the rate of maternal preeclampsia, maternal-perinatal outcomes, and sFlt-1, PLGF, sFlt-1/PlGF ratio and sEng levels. Results The rate of preeclampsia was (non-significantly) lower in the pravastatin group compared with the control group (17.5% vs 35%). The pravastatin group also had a (non-significant) lower rate of severe preeclampsia, HELLP syndrome, acute kidney injury and severe hypertension. The rate of (iatrogenic) preterm delivery was significantly (p=0.048) lower in the pravastatin group (n=4) compared with the controls (n=12). Neonates in the pravastatin group had significantly higher birthweights (2931 + 537 vs 2625 + 872 g; p=0.006), lower Apgar scores < 7 (2.5 vs 27.5%, p=0.002), composite neonatal morbidity (0 vs 20%, p=0.005) and NICU admission rates (0 vs 15%, p=0.026). All biomarkers show a significant deterioration in the control group compared with non significant changes in the pravastatin group. Conclusions Pravastatin holds promise in the secondary prevention of preeclampsia and placenta-mediated adverse perinatal outcomes by improving the angiogenic imbalance.

scholarly journals A Geriatrics-Focused Hospitalist Trauma Comanagement Program Improves Survival and Quality Measures for Older Adults: A Propensity Score-Matched Analysis

2021 ◽  
Author(s):  
Nasen J. Zhang ◽  
Liron Sinvani ◽  
Tung Ming Leung ◽  
Michael Qiu ◽  
Cristy L. Meyer ◽  
...  
Keyword(s):  
High Risk ◽  
Propensity Score ◽  
Older Adult ◽  
Quality Indicators ◽  
Intervention Group ◽  
Geriatric Trauma ◽  
Categorical Variables ◽  
Control Group ◽  
Trauma Patients ◽  
Medical Complexity

Abstract Background: Given the increasing age and medical complexity of trauma patients, medical comanagement has been adopted as a strategy for high-risk patients. This study aimed to determine whether a geriatrics-focused hospitalist trauma comanagement program improves outcomes.Methods: A pre- and post-implementation study compared older adult trauma patients who were comanaged by a hospitalist with those prior to comanagement at a Level 1 trauma center. Criteria for comanagement included: age 65+, multiple comorbidities, and use of high-risk medications. Comanagement focused on geriatric trauma management guidelines. One-to-one propensity score matching (PSM) was performed based on age, gender, Injury Severity Score, Charlson comorbidity index, and initial admission to the intensive care unit (ICU). Outcomes included hospital mortality, length of stay (LOS), and orders for geriatrics-focused quality indicators. Differences were compared with the Wilcoxon Rank Sum test for continuous variables and chi- square or Fisher’s exact test for categorical variables.Results: From 792 control and 365 intervention patients, PSM resulted in 290 matched pairs. Three intervention group patients died compared to 14 in the control group (p=0.0068). Hospital LOS, 30-day readmission, ICU LOS, and ICU upgrades were not significantly different between groups. There was an overall trend toward improved geriatrics-focused quality indicators in the intervention group. Intervention group was less likely to be restrained (p=0.04), received earlier physical therapy (p=0.01), more doses of acetaminophen compared to control patients (p<0.0001), and more subcutaneous enoxaparin rather than heparin (p=0.0027).Discussion: Our main findings highlight the higher medical complexity and increased risks in older adult trauma patients, as well as the mortality reduction and adherence increase to geriatrics-focused quality indicators. Limitations of our study included use of a single center, the possibility of selection bias in analyzing historical data, and a low sample size, all of which may limit generalizability. Conclusions: Our study demonstrates that a geriatrics-focused hospitalist trauma comanagement program improves survival and quality of care.

The Effect of Lullaby Intervention on Anxiety and Prenatal Attachment in Women with High-Risk Pregnancy: A Randomized Controlled Study

2021 ◽  
pp. 1-9
Author(s):  
Nazlı Baltacı ◽  
Mürüvvet Başer
Keyword(s):  
High Risk ◽  
Intervention Group ◽  
Group Versus ◽  
Mean Difference ◽  
High Risk Pregnancy ◽  
Controlled Study ◽  
Control Group ◽  
Prenatal Attachment ◽  
Randomized Controlled ◽  
Risk Pregnancy

<b><i>Background:</i></b> Women with high-risk pregnancy experience anxiety and low mother-fetal attachment when faced with signs of danger and health problems. This study aimed to investigate the effects of lullaby intervention on anxiety and prenatal attachment in women with high-risk pregnancy. <b><i>Materials and Methods:</i></b> This randomized controlled trial was conducted in the perinatology clinic of a state maternity hospital in Turkey. Seventy-six women with high-risk pregnancy were included. The intervention group listened to lullabies for 20 min once a day, and accompanied by lullabies touched their abdomen and thought about their babies, but the control group did not. Data were collected using the Pregnant Information Form, the State Anxiety Inventory, and the Prenatal Attachment Inventory. <b><i>Results:</i></b> Baseline anxiety did not differ in the intervention versus control group (47.83 ± 10.74 vs. 44.10 ± 8.08, mean difference 3.73 [95% Cl –1.18 to 8.64], <i>p</i> = 0.13), but after the 2nd day lullaby intervention anxiety was lower in the intervention group versus control group (33.66 ± 9.32 vs. 43.06 ± 8.10, mean difference –9.40 [95% Cl –13.91 to –4.88], <i>p</i> &#x3c; 0.01). Baseline prenatal attachment did not differ in the intervention versus control group (56.03 ± 10.71 vs. 53.86 ± 9.98, mean difference 2.16 [95% Cl –3.18 to 7.51], <i>p</i> = 0.42), but after the 2nd day lullaby intervention prenatal attachment was higher in the intervention group versus control group (66.70 ± 7.60 vs. 54.36 ± 9.52, mean difference 12.33 [95% Cl 7.87 to 16.78], <i>p</i> &#x3c; 0.01). In the within-group analysis the intervention group had lower anxiety and better prenatal attachment (<i>p</i> &#x3c; 0.01), but not in the control group (<i>p</i> &#x3e; 0.05). <b><i>Conclusion:</i></b> Lullaby intervention can play an effective role in reducing anxiety and improving prenatal attachment. The use of this integrative, noninvasive, non-pharmacologic, time-efficient, and natural intervention is suggested in the care of pregnant women.

Abstract 225: A Depression Quality Program and Cardiovascular Disease: Assessing the Impact on Care

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Linda Gordon ◽  
Amanda Malecky ◽  
Andrew Althouse ◽  
Nicole Ansani
Keyword(s):  
Usual Care ◽  
Goal Attainment ◽  
Intervention Group ◽  
Phone Call ◽  
Categorical Variables ◽  
Care Group ◽  
Control Group ◽  
Surrogate Outcome ◽  
The Impact

Background: Data demonstrate an adverse association between depression and coronary artery disease prognosis. Therefore, a depression screening program was initiated in the catheterization (cath) lab. The goals were to improve HEDIS depression compliance rates and determine the impact on clinical outcomes. Methods: Adult patients in an inpatient cath lab from 3 cardiology practices were screened for enrollment in a randomized controlled trial. All cath lab patients received a PHQ-9 depression screener. Those who screened positive for depression (score ≥ 10) were randomized to intervention or usual care. The usual care group received a follow-up phone call to re-administer the PHQ-9 at 6-8 weeks and within 210 days of discharge. The intervention group was administered the PHQ-9 and received intensive education at baseline, 6-8 weeks, and within 210 days of discharge. Education included targeted depression information with a mental health care provider and comprehensive disease management education with a cardiovascular nurse practitioner. Outcomes included: differences in HEDIS depression goal attainment; depression response/remission rates; and cardiovascular goals. Differences between groups were tested using chi-squared tests (categorical variables) and t-tests (continuous variables). Results: Baseline characteristics were similar between control (N=43) and intervention (N=40) groups, with the exception of significantly fewer African American patients in the control group (N=2, 4.7%) vs intervention (N=9, 22.5%). Changes in HEDIS goal attainment show that patients in the intervention group were slightly more likely to be referred to a provider to address depression (95.0% vs 86.0%, p=0.314), or receive meds for depression (65.0% vs 51.2%, p=0.219), but these differences are not statistically significant. More patients in the intervention group refused meds for depression compared to control (15.0% vs. 2.3%, p=0.041); have received blood work (65.0% vs 41.9%, p=0.030); and have received follow-up within 210 days (82.5% vs 46.5%, p<0.001). Treatment adjustment rate was higher in the intervention group compared to control (85.0% vs. 65.1%, p=0.037). Hospital readmission rate was similar between groups (p=0.896) and there was no difference in depression remission or response rates (p=0.426). Further, no differences were seen in cardiovascular surrogate outcome parameters, including cholesterol, A1c, CRP, or BNP between groups; except SGOT was significantly different between groups (-5.0 intervention vs 2.0 control p=0.045). Conclusions: These data demonstrate improvements in attaining a surrogate outcome measure of quality (HEDIS goals); however, this does not appear to translate to a significant clinical impact. Quality measures may need to be continuously reassessed to ensure efficiency and effectiveness of care.

Abstract TP168: Efficacy of a Pictogram to Promote Stroke Knowledge Among Illiterate Korean Elderly

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Cha-Nam Shin ◽  
Jeongha Sim ◽  
Dongchoon Ahn
Keyword(s):  
Older Adults ◽  
High Risk ◽  
Longitudinal Research ◽  
Controlled Trial ◽  
Intervention Group ◽  
Warning Signs ◽  
Repeated Measure ◽  
Control Group ◽  
Control Group Design ◽  
Stroke Knowledge

Background and Purpose: Extensive research supports the importance of knowledge in stroke prevention and reducing prehospital delay time. However, the level of stroke knowledge among Korean older adults remains low. In particular, older adults who are illiterate lack of stroke information despite being at high risk. The purpose of this study was to develop and examine the efficacy of a pictogram to enhance stroke knowledge in the high-risk and illiterate older adults. Methods: We conducted a pretest-posttest nonequivalent control group design study and compared differences in stroke knowledge before and after the intervention. A total of 117 older adults (82 in the intervention group and 35 in the control group) who were 60 years and older residing in community participated in the study. Participants in the intervention group received a pictogram-based education, while participants in the control group received a powerpoint-based education. Stroke knowledge was measured by structured survey questionnaires. Descriptive statistics for sample characteristics, repeated measure ANOVA for the efficacy, and independent t-test for satisfaction comparison between groups were used. Results: The intervention group showed a higher increase in stroke knowledge (F=16.45), awareness of risk factors (F=15.71), stroke warning signs and symptoms (F=17.29), and action at stroke (F=19.36) compared to the control group at p <.001. Also, the intervention group reported that they would recommend the education program to others (t=2.64, p<.05) and the program was applicable to real situation (t=4.47, p <.001), which were scored higher than the control group. Conclusions: The data revealed that a pictogram-based education is more effective than a powerpoint-based education among illiterate older adults. Replicated studies with this pictogram in a larger randomized controlled trial is warranted, which may give greater validity to our findings. Future longitudinal research is recommended to examine retention of stroke knowledge over the long term.

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