Of the original 103,898 members within the Hawaii subset of the MEC, 10,028 (9.7%) subjects who reported a diagnosis of diabetes at baseline (Table 1 ) and 10 subjects who had missing information were excluded from the incidence analysis (Fig. 1 ). Subjects who indicated having diabetes at any point after baseline or who were classified as case subjects by one of the health plans were considered incident at the time of the first report. Individuals who never reported diabetes and who were not identified as diabetic patients by the health plans were categorized as noncase subjects. As described above, data on diabetes status were available at three subsequent time points from four different sources: the FuQx, the MedQx, and the linkage with the BCBS and KP health plans. Of the 86,732 participants who completed the FuQx, 9,964 (11.5%) indicated diabetes. At the time of the MedQx, 4,425 (11.1%) of the 39,787 subjects reported use of diabetes medications. Finally, of the 88,004 MEC subjects linked with the BCBS plan, 11,375 were identified as diabetic case subjects (16.9% of estimated BCBS members, i.e., 88,004 minus 20,539 KP members), while 20,539 (23.3%) MEC subjects were identified as KP members of whom 4,003 (19.5%) were diabetic case subjects.
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Incident Reporting
Incident Reporting
Incident Reporting is a critical process for organizations to effectively manage and mitigate risks.
It involves the systematic documentation and analysis of incidents, accidents, or other events that can impact an organization's operations, safety, or compliance.
By leveraging AI-powered tools like PubCompare.ai, researchers and professionals can streamline their incident reporting workflow, easily locate relevant protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products for their needs.
This innovative solution helps organizations make informed decisions, optimize their incident reporting processes, and enhance their overall risk management strategies.
With PubCompare.ai, users can discover how to effeciently compare incident reporting protocols, leveraging the power of AI to improve their decision-making and ensure the safety and compliance of their operations.
It involves the systematic documentation and analysis of incidents, accidents, or other events that can impact an organization's operations, safety, or compliance.
By leveraging AI-powered tools like PubCompare.ai, researchers and professionals can streamline their incident reporting workflow, easily locate relevant protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products for their needs.
This innovative solution helps organizations make informed decisions, optimize their incident reporting processes, and enhance their overall risk management strategies.
With PubCompare.ai, users can discover how to effeciently compare incident reporting protocols, leveraging the power of AI to improve their decision-making and ensure the safety and compliance of their operations.
Most cited protocols related to «Incident Reporting»
Diabetes Mellitus
Diagnosis
Health Planning
Incident Reporting
Patients
Pharmaceutical Preparations
An iterative mixed-methods approach was used for tool development, described in detail elsewhere.14 The typology of mistreatment provided the structure, domains, and items.5 Both tools are available in open access.14 Data were collected using digital, tablet-based tools (BLU Studio XL2, Android, BLU Products, Miami, FL, USA).
The labour observation tool has three forms completed and submitted at different times: (1) admission form; (2) incident report form; and (3) childbirth, interventions, and discharge form.14 The admission form was completed once (immediately after enrolment) for all women, and included screening questions and sociodemographics. The incident report form was completed for the following events: physical or verbal abuse, stigma or discrimination, or vaginal examination, and could be submitted multiple times (repeating form for multiple events). For physical or verbal abuse and stigma or discrimination, the incident report included the timing and type of provider involved. For vaginal examinations, information was collected about consent, privacy, and confidentiality. The childbirth, interventions, and discharge form was completed once at the end of the observation for all women, and included pain relief, mobilisation, fluids, companionship, fees, neglect, privacy, health outcomes, and interventions.
The survey tool had two forms completed and submitted at different times. The screening form assessed eligibility.14 The survey form was completed during survey administration,14 and included sociodemographics, birth experiences (including mistreatment, vaginal examinations, companionship, and pain relief), health outcomes, interventions, post-partum depression, and satisfaction with care.
The labour observation tool has three forms completed and submitted at different times: (1) admission form; (2) incident report form; and (3) childbirth, interventions, and discharge form.14 The admission form was completed once (immediately after enrolment) for all women, and included screening questions and sociodemographics. The incident report form was completed for the following events: physical or verbal abuse, stigma or discrimination, or vaginal examination, and could be submitted multiple times (repeating form for multiple events). For physical or verbal abuse and stigma or discrimination, the incident report included the timing and type of provider involved. For vaginal examinations, information was collected about consent, privacy, and confidentiality. The childbirth, interventions, and discharge form was completed once at the end of the observation for all women, and included pain relief, mobilisation, fluids, companionship, fees, neglect, privacy, health outcomes, and interventions.
The survey tool had two forms completed and submitted at different times. The screening form assessed eligibility.14 The survey form was completed during survey administration,14 and included sociodemographics, birth experiences (including mistreatment, vaginal examinations, companionship, and pain relief), health outcomes, interventions, post-partum depression, and satisfaction with care.
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Childbirth
Depression, Postpartum
Discrimination, Psychology
Drug Abuse
Eligibility Determination
Fingers
Incident Reporting
Pain
Patient Discharge
Physical Examination
Satisfaction
Tablet
Vaginal Examination
Woman
We used three sources of data in this analysis. First, weekly reports of HFMD incidence between 1 January 2009 and 31 December 2013 (a total of 262 wk) were obtained from a national surveillance system maintained by the Chinese Center for Disease Control and Prevention (China CDC) in Beijing, China. These reports were available at two spatial scales: the seven administrative regions and 31 provinces of China.
Second, we obtained time series of weekly laboratory-confirmed HFMD for a sub-sample of cases from each province, with virological test results classified as EV-A71, CV-A16, other non-EV-A71 and non-CV-A16 serotypes of enterovirus, or negative [15 (link)]. This information was aggregated to the regional scale on a monthly basis to reduce potential biases from the sampling scheme (S1 Fig ). The proportions of each serotype were estimated, with all positive and negative tests included in the denominator. If there were no virological test data available for a given month, the proportions from the virological tests of the previous month were substituted. We applied these proportions to the reported case counts from the surveillance registry to estimate serotype-specific weekly incidence by province (Fig 1 ; S2 Dataset ). Since infection with EV-A71 or CV-A16 accounted for the majority of total laboratory-confirmed HFMD cases between 2008 and 2013 (73%), we limited the scope of the main analysis to infection with either of these two serotypes. We used a time step of 1 wk: the incubation period of HFMD is 7 to 14 d and viral excretion persists for about 2 wk after symptom onset, so the generation time would be contained in this time frame. It has previously been shown that the estimation of seasonality is robust to the length of the chosen time step [43 (link)].
Third, we obtained yearly birth rates and population sizes between 2009 and 2013 by region and by province from the National Bureau of Statistics of China (S1 Dataset ) [44 ]. Although reports of HFMD cases were available from 2008, the time frame of this analysis was limited to the period between 1 January 2009 and 31 December 2013 because of the sparsity of laboratory-confirmed cases during the first year of surveillance.
Second, we obtained time series of weekly laboratory-confirmed HFMD for a sub-sample of cases from each province, with virological test results classified as EV-A71, CV-A16, other non-EV-A71 and non-CV-A16 serotypes of enterovirus, or negative [15 (link)]. This information was aggregated to the regional scale on a monthly basis to reduce potential biases from the sampling scheme (
Third, we obtained yearly birth rates and population sizes between 2009 and 2013 by region and by province from the National Bureau of Statistics of China (
Full text: Click here
Chinese
Enterovirus 71, Human
Enterovirus Infections
Incident Reporting
Infection
Reading Frames
We extracted information on the title, length of the video (in minutes), total views, days since upload, likes and dislikes. Calculations of views/day were performed. The videos was further divided into four categories [16 (link), 17 (link)]: Education; Entertainment; News & Politics; People & Blogs. More specifically, medical courses or other academic videos were divided into “Education”; comedies and talk shows were divided into “Entertainment”; videos form government agencies and news reports about food poisoning incidents or outbreaks were divided into “News & Politics”; videos depicting personal food poisoning experiences or videos showing personal opinions about food poisoning were divided into “personal & blogs”. For example, a video describing the difference between a stomach flu and food poisoning (https://www.youtube.com/watch?v=EC7UaLIAEP4 ) was divided into “Education”.
A customized usefulness scoring scheme was created where scores were given for video quality and specific content. The Global Quality Scale (GQS) [18 (link)] was used to assess the overall quality of all selected videos. As shown in Table1 , GQS was a five-point Likert scale based on the quality of information, the flow and ease of use of the information present online. Seven content-specific items were also developed to assess whether videos discussed risk factors, epidemiology, etiology, symptoms, diagnosis, treatment and prevention of food poisoning. According to the video details of each item, the video was given 0 point (Not mentioned), 1 point (Briefly introduced) and 2 points (Introduced in detail). The total usefulness score is the sum of the GQS score and content score. According to the total score, the videos was categorized as slightly useful (1–6), useful (7–13), very useful (14–19).
Each video was scored by two independent viewers (M. Li, S.M. Yan) who were knowledgeable in the risk factors, epidemiology, etiology, symptoms, diagnosis, treatment and prevention of food poisoning. If the content scores or GQS scores given by two viewers differ by three or more points, then the final score is given by an arbitrator (W.W. Cui). In addition to the scores given by the arbitrator, the scores given by the two viewers were then averaged to give an overall score that was used for final results and statistical analysis.
A customized usefulness scoring scheme was created where scores were given for video quality and specific content. The Global Quality Scale (GQS) [18 (link)] was used to assess the overall quality of all selected videos. As shown in Table
Global Quality Scale Criteria Used to Score Videos Containing Information About Food Poisoning
Score | Global Score Description |
---|---|
1 | Poor quality, poor flow of the site, most information missing, not at all useful for patients |
2 | Generally poor quality and poor flow, some information listed but many important topics missing, of very limited use to patients |
3 | Moderate quality, suboptimal flow, some important information is adequately discussed but others poorly discussed, somewhat useful for patients |
4 | Good quality and generally good flow, most of the relevant information is listed, but some topics not covered, useful for patients |
5 | Excellent quality and excellent flow, very useful for patients |
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Diagnosis
Disease Outbreaks
Incident Reporting
Speech
Stomach
Each incident report underwent data coding using multi-axial frameworks to describe incident types (primary and contributory), potential contributory factors, incident outcomes, and harm severity (S2 –S4 Texts) [23 ,25 (link),28 (link)]. Primary incidents included those proximal (chronologically) to the patient outcome, whereas contributory incidents included those that contributed to the occurrence of another incident. Multiple codes for incident type, contributory factor, and incident outcome were applied to each report where necessary. The codes were applied systematically and chronologically according to nine recursive incident analysis rules developed by the Australian Patient Safety Foundation (S1 Table ) [29 ]. This permitted modeling of the steps preceding and leading to primary incidents, e.g., contributory incidents and factors, which, in turn, resulted in patient outcomes (S1 Fig ). The incident type, contributory factor, and incident outcome frameworks were developed in house [28 (link)]. Each incident report in the NRLS comes with a reporter-allocated harm severity; however, where the free-text descriptions conflicted with the reporter-allocated harm severity, harm severity was reclassified using WHO International Classification for Patient Safety definitions (see Table 1 for WHO definitions of harm severity) [3 ,23 ,25 (link),30 ]. The medications involved in medication incidents were recorded and classified using the British National Formulary for Children, and the types of conditions affecting these children were recorded and classified using the International Classification of Diseases (ICD-10) (S2 Table ) [31 ,32 ]. A random 20% sample of reports was independently double-coded by P. R. and H. W.
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Child
Incident Reporting
Patients
Patient Safety
Pharmaceutical Preparations
Most recents protocols related to «Incident Reporting»
Questionnaire data were collected prior to the implementation of PEWS and SBAR in January 2018 and at follow-up 6 months after the completion of the implementation period in January 2021. All the data were obtained from the electronic medical records in the Information System of Hospital. The information of the patients during admission including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables was collected. The pneumonia symptom relief time and total hospitalization time of fever, cough, pulmonary rales, and wheezing were recorded in both groups. Emotional state: The Neonatal Infant Pain Scale (NIPS)[9 (link)] and the Neonatal Behavior Neurological Assessment Scale (NBNA)[10 (link)] were used to evaluate the emotional state of the newborn by the senior nurses who had been specially trained and qualified as psychotherapists. The number of intervention measures reported by nursing staff and the number of medical intervention measures during PICU in the 2 groups was counted, and then the correct recognition rate of observation and early recognition rate of critically ill children were calculated. The early recognition rate of severe patients = times of rescue of critically ill children/ (total number of reports - times of repeated reports) × 100%. The incidence of handover problems during PICU was recorded between the 2 groups, including incident reports and communication errors. In accordance with World Health Organization definitions, we defined incident reports as “A process used to document occurrences that are not consistent with routine hospital operation or patient care. A communication error is defined as “Missing or wrong information exchange or misinterpretation or misunderstanding.”[11 (link)] To measure communication within and between different professions, the ICU Nurse–Physician Questionnaire was used.[12 (link)]
Child
Consciousness
Cough
Critical Illness
Emotions
Fever
Gender
Hospitalization
Incident Reporting
Infant, Newborn
Neurologic Examination
Nurses
Nursing Staff
Pain
Patients
Physicians
physiology
Pneumonia
Psychotherapists
Signs, Vital
The occurrence of specific serious adverse events (SAEs) according to War Child’s operational definition (including deaths; suicide attempts; victimization including physical, sexual and emotional abuse or neglect; serious violence; emergency psychiatric or medical hospitalisation; or serious lack of food) and adverse events (AEs; including injuries or accidents on way to or from the research activities; marked increases in suicidal thoughts; mentioning of concrete and detailed plan to commit suicide; marked increases in emotional distress; marked increases in conflicts within family or community; other violence towards staff or participants) were monitored throughout the study by field-based research and implementation teams. They were reported using structured incident report forms submitted to the lead investigators, who then reported to the Data Safety Management Committee (DSMC) and relevant ethical boards. The trial coordinator provided daily supervision and oversight to assessors during data collection. Weekly study meetings between the research coordinator and study investigators ensured adequate support for implementation, fidelity to protocol, and trial safety.
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Abuse, Emotional
Accidents
Child
Emergencies
Food
Incident Reporting
Injuries
Physical Examination
Psychological Distress
Safety
Suicide Attempt
Supervision
Victimization
ARI reports were summarized for the study population by week and age group from March 1, 2020, through June 30, 2021. Incidence rates (IR) of ARI reports and virus detections were calculated per 100 person‐years for the study period and for a pre‐pandemic period of similar length (March 1, 2016, to June 30, 2017); these rates were compared using incidence rate ratios (IRR) with 95% confidence intervals (CI). The detection of each respiratory virus as a proportion of reported ARIs was determined overall and compared by age group (<5, 6–11, 12–17, 18–49, ≥50 years). For analytic purposes, child is defined as age <18 years. An epidemic curve for the study period was generated by plotting ARI reports by RT‐PCR test result. Reported COVID‐19 cases in Michigan were obtained from the MI Safe Start Map (https://www.mistartmap.info/ ) and were used to indicate when COVID‐19 incidence exceeded 150 cases per million population per day. Dates when the “Stay Home, Stay Safe” order was implemented and when K‐12 schools were closed were also obtained from the MI Safe Start Map.
Frequencies of survey responses were calculated and compared for each survey period. Serology data were combined into three serosurveillance periods: summer 2020, fall 2020–winter 2021, and spring 2021. Seroprevalence of antibodies against SARS‐CoV‐2 N protein was determined overall and by age group. Statistical analysis was performed using SAS (v9.4, SAS Institute Inc., Cary, NC) and R statistical software (v4.1.0; R core team, 2021).
Frequencies of survey responses were calculated and compared for each survey period. Serology data were combined into three serosurveillance periods: summer 2020, fall 2020–winter 2021, and spring 2021. Seroprevalence of antibodies against SARS‐CoV‐2 N protein was determined overall and by age group. Statistical analysis was performed using SAS (v9.4, SAS Institute Inc., Cary, NC) and R statistical software (v4.1.0; R core team, 2021).
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Age Groups
Antibodies
Child
COVID 19
Epidemics
Incident Reporting
nucleocapsid phosphoprotein, SARS-CoV-2
Pandemics
Respiratory Rate
Reverse Transcriptase Polymerase Chain Reaction
Virus
Complications associated with volar plate injuries to the PIPJ (Eaton type I, II, IIIA) are commonly reported in the literature and include FFD of the PIPJ, PIPJ extension lag, persistent pain, persistent oedema, reduced flexion and reduced grip strength.
These common complications are usually managed through hand therapy intervention. The hand therapists at Sydney Hospital Hand Unit are experienced therapists able to identify and treat these complications. In addition, all treating and assessing hand therapists will be provided with training that will include identification and treatment of these impairments. A treatment guideline has been developed to assist with managing these impairments.
Less common complications associated with volar plate injuries to the PIPJ include instability and re-dislocation of the PIPJ. These complications are not anticipated to occur as joints that are unstable are more likely to be associated with Eaton Type IIIb and are excluded from this study.
If an unanticipated event occurs these will be reported back to the researcher by face- to-face communication; email or phone. The researcher is on site, which should make reporting of incidents from a research point of view easy and efficient. The assessing therapists will also be able to identify complications at each assessment point using the assessment sheet; if a re-dislocation occurs an incident report should be lodged by the treating/assessing therapist. If an incident report is made the researcher will be notified via email. This is as per standard clinical practice within the hand unit at Sydney Hospital.
In terms of clinical management of less common complications these will be managed as per standard practice. There are clear management guidelines established within the hand unit at Sydney Hospital and include; reviewing the patient with a senior hand therapist in the team; organising a medical review for the patient by liaising with a person in the medical team in the hand clinic, by contacting the on-call hand surgeon and organising an urgent review and by lodging an incident report via the online reporting system.
A document will be used to record common and uncommon complications in both groups and reviewed by the investigative team every 3 months. Any strong imbalance between treatment groups will be reported to the clinical head of the Hand Unit and the Ethics Committee who may terminate the trial.
These common complications are usually managed through hand therapy intervention. The hand therapists at Sydney Hospital Hand Unit are experienced therapists able to identify and treat these complications. In addition, all treating and assessing hand therapists will be provided with training that will include identification and treatment of these impairments. A treatment guideline has been developed to assist with managing these impairments.
Less common complications associated with volar plate injuries to the PIPJ include instability and re-dislocation of the PIPJ. These complications are not anticipated to occur as joints that are unstable are more likely to be associated with Eaton Type IIIb and are excluded from this study.
If an unanticipated event occurs these will be reported back to the researcher by face- to-face communication; email or phone. The researcher is on site, which should make reporting of incidents from a research point of view easy and efficient. The assessing therapists will also be able to identify complications at each assessment point using the assessment sheet; if a re-dislocation occurs an incident report should be lodged by the treating/assessing therapist. If an incident report is made the researcher will be notified via email. This is as per standard clinical practice within the hand unit at Sydney Hospital.
In terms of clinical management of less common complications these will be managed as per standard practice. There are clear management guidelines established within the hand unit at Sydney Hospital and include; reviewing the patient with a senior hand therapist in the team; organising a medical review for the patient by liaising with a person in the medical team in the hand clinic, by contacting the on-call hand surgeon and organising an urgent review and by lodging an incident report via the online reporting system.
A document will be used to record common and uncommon complications in both groups and reviewed by the investigative team every 3 months. Any strong imbalance between treatment groups will be reported to the clinical head of the Hand Unit and the Ethics Committee who may terminate the trial.
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Debility
Edema
Ethics Committees
Face
Grasp
Head
Incident Reporting
Injuries
Joint Dislocations
Joints
Manual Therapies
Pain
Patients
Surgeons
The annual financial impact of adopting DOACs for treating VTE in a Thai healthcare setting was estimated using a budget impact analysis. The budget impact was calculated with a 5-year time horizon and the 6-month medication treatment costs. The incidence of VTE patients and the number of Thai people aged over 60 years were used to estimate the annual number of VTE patients in Thailand. As there were no reports of VTE incidence in Thailand, the incidence of VTE from an Asian country was used [51 (link)]. The 5% DOAC uptake rate was assumed to calculate the number of VTE patients receiving DOACs each year. The assumption was consistent with a previous budget impact analysis study [52 (link)]. The inputs for the budget impact are presented in Table S2 in the Additional File.
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Asian Persons
Incident Reporting
Patients
Pharmaceutical Preparations
Thai
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More about "Incident Reporting"
Incident reporting is a critical process for organizations to effectively manage and mitigate risks.
It involves the systematic documentation and analysis of incidents, accidents, or other events that can impact an organization's operations, safety, or compliance.
This process is essential for identifying and addressing potential issues before they escalate, ensuring the safety and well-being of employees, customers, and the organization as a whole.
Leveraging AI-powered tools like PubCompare.ai, researchers and professionals can streamline their incident reporting workflow, easily locate relevant protocols from literature, pre-prints, and patents, and utilize AI-driven comparisons to identify the best protocols and products for their specific needs.
This innovative solution helps organizations make informed decisions, optimize their incident reporting processes, and enhance their overall risk management strategies.
By using PubCompare.ai, users can discover how to efficiently compare incident reporting protocols, leveraging the power of artificial intelligence to improve their decision-making and ensure the safety and compliance of their operations.
This tool can be particularly useful for organizations that rely on software like Stata 15, Stata 11, Cerner PowerChart, SPSS version 25, Stata V.15, Excel 2010, SPSS Statistics for Windows, Version 22.0, PASW Statistics for Windows, R version 3.6.1, or R ver. 4.1.0 to manage their data and operations.
Effective incident reporting is not just about documenting events; it's about using that information to drive meaningful change and improvements within the organization.
By incorporating AI-powered tools like PubCompare.ai into their incident reporting workflow, organizations can enhance their risk management strategies, optimize their processes, and ultimately, create a safer and more compliant environment for their stakeholders.
It involves the systematic documentation and analysis of incidents, accidents, or other events that can impact an organization's operations, safety, or compliance.
This process is essential for identifying and addressing potential issues before they escalate, ensuring the safety and well-being of employees, customers, and the organization as a whole.
Leveraging AI-powered tools like PubCompare.ai, researchers and professionals can streamline their incident reporting workflow, easily locate relevant protocols from literature, pre-prints, and patents, and utilize AI-driven comparisons to identify the best protocols and products for their specific needs.
This innovative solution helps organizations make informed decisions, optimize their incident reporting processes, and enhance their overall risk management strategies.
By using PubCompare.ai, users can discover how to efficiently compare incident reporting protocols, leveraging the power of artificial intelligence to improve their decision-making and ensure the safety and compliance of their operations.
This tool can be particularly useful for organizations that rely on software like Stata 15, Stata 11, Cerner PowerChart, SPSS version 25, Stata V.15, Excel 2010, SPSS Statistics for Windows, Version 22.0, PASW Statistics for Windows, R version 3.6.1, or R ver. 4.1.0 to manage their data and operations.
Effective incident reporting is not just about documenting events; it's about using that information to drive meaningful change and improvements within the organization.
By incorporating AI-powered tools like PubCompare.ai into their incident reporting workflow, organizations can enhance their risk management strategies, optimize their processes, and ultimately, create a safer and more compliant environment for their stakeholders.