scholarly journals Preanalytical errors in medical laboratories: a review of the available methodologies of data collection and analysis

2016 ◽  
Vol 54 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Jamie West ◽  
Jennifer Atherton ◽  
Seán J Costelloe ◽  
Ghazaleh Pourmahram ◽  
Adam Stretton ◽  
...  
Keyword(s):  
Patient Safety ◽  
Quality Management ◽  
Data Collection ◽  
Quality Indicators ◽  
Management Systems ◽  
Continual Improvement ◽  
Laboratory Errors ◽  
Data Collection Methods ◽  
The Impact ◽  
Collection Methods

Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture.

scholarly journals Analysis of the Impact of Teacher Program Direktorat PSMK(SM3T Program) to Animo Learning Students in Biological Lessons In SMKN 1 Kintamani, Bali

BIODIK ◽  
2018 ◽  
Vol 4 (2) ◽  
pp. 77-82
Author(s):  
Ervan Johan Wicaksana ◽  
Pramana Atmadja
Keyword(s):  
Regression Analysis ◽  
Data Collection ◽  
Descriptive Study ◽  
Biology Teacher ◽  
Data Collection Methods ◽  
Quantitative Descriptive ◽  
Learning Interest ◽  
The Impact ◽  
Collection Methods

This analytic descriptive study aims to determine the impact of the accompanying teacher of the SM3T program towards on the learning interest of the students in biology subject at SMKN 1 Kintamani, Bangli, Province of Bali. Data collection methods with observation, questionnaires and interviews. Data were analyzed by quantitative descriptive. The results of the regression analysis between the presence of the accompanying teacher and the learning interest of the students in Biology subject get a determination value of 0.28. These data indicate that 28% of the learning interest of students at SMK 1 Kintamani is influenced by the presence of a companion biology teacher in the Direktorat PSMK program.   Abstrak. Penelitian deskriptif analitik ini bertujuan untuk mengetahui dampak program guru pendamping Direktorat PSMK (Program SM3T) terhadap animo belajar peserta didik pada mata pelajaran biologi di SMKN 1 Kintamani, kabupaten Bangli, Propinsi Bali. Metode pengumpulan data dengan observasi, angket dan wawancara. Data dianalisis dengan deskriptif kuantitatif. Hasil analisis regresi antara adanya guru pendamping dengan animo belajar peserta didik pada mata pelajaran Biologi mendapatkan nilai determinasi sebesar 0,28. Data tersebut menenuntukkan bahwa 28% animo belajar peserta didik di SMKN 1 Kintamani dipengaruhi oleh adanya guru biologi pendamping pada program Direktorat PSMK.   Kata Kunci: Guru Pendamping, Program Direktorat PSMK, Animo Belajar

scholarly journals Dampak Kebijakan Pandemi Covid-19 Pada Masjid Raya

2021 ◽  
Vol 1 (3) ◽  
pp. 190-221
Author(s):  
Muhammad Saerozi
Keyword(s):  
Data Collection ◽  
Mixed Method ◽  
Group Discussion ◽  
Discussion Forums ◽  
Qualitative And Quantitative ◽  
Data Collection Methods ◽  
The Impact ◽  
Collection Methods

The Covid-19 pandemic that has hit Indonesia since March 2109 has an impact on all aspects of life, including in this study the impact on mosque cash. This study aims to analyze the trend of obtaining mosque cash funds at the Istiqomah Ungaran Grand Mosque before and during the covid-19 pandemic, if there is a decline then what causes the decline to occur and what philanthropic strategies are made by mosque administrators to optimize the mosque's cash funds. This research was completed by using a mixed method qualitative and quantitative with data collection methods through observation, interviews, documentation and group discussion forums (FGD). This study found that a significant decrease occurred in the cash fund of the Great Mosque of Istiqomah Ungaran due to the reduced activities organized by the mosque. The strategy made by the administrators of the Istiqomah Grand Mosque in Ungaran is with online recitations as well as infaq payments and online Istiqomah RA and SD Istiqomah tuition payments.  

scholarly journals Epidemiology of constipation in adults: Why estimates of prevalence differ

2019 ◽  
Vol 5 (1) ◽  
pp. 37
Author(s):  
Barry Werth
Keyword(s):  
Data Collection ◽  
Older Adult ◽  
Epidemiological Studies ◽  
Community Dwelling ◽  
Data Collection Methods ◽  
Wide Range ◽  
Prevalence Estimates ◽  
Age Range ◽  
The Impact ◽  
Collection Methods

This review of over 80 articles published in the last 30 years shows that estimates of the prevalence of chronic constipation in community-dwelling adults varied widely from 2.4% to 39.6% in general adult populations and from 4% to 25.8% in older adult populations. Estimates of the prevalence of any constipation (including both chronic and sporadic constipation) also varied widely from 2.6% to 31.0% in general adult populations and from 4.4% to 44.5% in older adult populations. Apart from any country or regional differences, this wide range of estimated prevalence may be attributed to different definitions used for both chronic and any constipation as well as different data collection methods and sampling differences. Sampling issues include sample size, representativeness and age range of populations sampled. Further research is required to examine the impact of different definitions on prevalence estimates to help determine the best definitions for use in future epidemiological studies. If standard definitions can be universally agreed and used, along with appropriate sampling and data collection methods, more precise estimates of constipation prevalence should be attained. This would allow more meaningful comparisons between countries and may also provide the ability to pool results.

Contracting for Pavement Distress Data Collection

1998 ◽  
Vol 1643 (1) ◽  
pp. 80-85 ◽  
Author(s):  
Roger E. Smith ◽  
Thomas J. Freeman ◽  
Olga J. Pendleton
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Data Collection ◽  
Pavement Management ◽  
Management Systems ◽  
Automated Data Collection ◽  
Pavement Distress ◽  
Data Collection Methods ◽  
Pavement Management Systems ◽  
Collection Methods

Many agencies responsible for managing pavements have adopted pavement management systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS is collecting condition information, especially pavement distress information. Many agencies have started to contract for pavement distress data collection. Some of the agencies have experienced problems with the data collected by contract. A study for agencies in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described. Issues about quality control and quality assurance faced by agencies considering contracting for automated data collection also are raised. These issues need additional study to develop appropriate guidelines. The initial set provided is based on discussions with some of the agencies currently contracting for pavement distress data collection.

scholarly journals Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review (Preprint)

2017 ◽  
Author(s):  
Claire McCallum ◽  
John Rooksby ◽  
Cindy M Gray
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Repeated Measures ◽  
Single Case ◽  
Optimization Strategy ◽  
Health Technologies ◽  
Data Collection Methods ◽  
Research Designs ◽  
The Impact ◽  
Collection Methods

BACKGROUND Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. OBJECTIVES This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. METHOD An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. RESULTS A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). CONCLUSIONS The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines.

scholarly journals Quality Indicators in Laboratory Medicine: the status of the progress of IFCC Working Group “Laboratory Errors and Patient Safety” project

2017 ◽  
Vol 55 (3) ◽  
Author(s):  
Laura Sciacovelli ◽  
Giuseppe Lippi ◽  
Zorica Sumarac ◽  
Jamie West ◽  
Isabel Garcia del Pino Castro ◽  
...  
Keyword(s):  
Patient Safety ◽  
Data Collection ◽  
Quality Indicators ◽  
Working Group ◽  
State Of The Art ◽  
Laboratory Medicine ◽  
Error Rates ◽  
International Standard ◽  
Laboratory Errors ◽  
The Individual

AbstractThe knowledge of error rates is essential in all clinical laboratories as it enables them to accurately identify their risk level, and compare it with those of other laboratories in order to evaluate their performance in relation to the State-of-the-Art (i.e. benchmarking) and define priorities for improvement actions. Although no activity is risk free, it is widely accepted that the risk of error is minimized by the use of Quality Indicators (QIs) managed as a part of laboratory improvement strategy and proven to be suitable monitoring and improvement tools. The purpose of QIs is to keep the error risk at a level that minimizes the likelihood of patients. However, identifying a suitable State-of-the-Art is challenging, because it calls for the knowledge of error rates measured in a variety of laboratories throughout world that differ in their organization and management, context, and the population they serve. Moreover, it also depends on the choice of the events to keep under control and the individual procedure for measurement. Although many laboratory professionals believe that the systemic use of QIs in Laboratory Medicine may be effective in decreasing errors occurring throughout the total testing process (TTP), to improve patient safety as well as to satisfy the requirements of International Standard ISO 15189, they find it difficult to maintain standardized and systematic data collection, and to promote continued high level of interest, commitment and dedication in the entire staff. Although many laboratories worldwide express a willingness to participate to the Model of QIs (MQI) project of IFCC Working Group “Laboratory Errors and Patient Safety”, few systematically enter/record their own results and/or use a number of QIs designed to cover all phases of the TTP. Many laboratories justify their inadequate participation in data collection of QIs by claiming that the number of QIs included in the MQI is excessive. However, an analysis of results suggests that QIs need to be split into further measurements. As the International Standard on Laboratory Accreditation and approved guidelines do not specify the appropriate number of QIs to be used in the laboratory, and the MQI project does not compel laboratories to use all the QIs proposed, it appears appropriate to include in the MQI all the indicators of apparent utility in monitoring critical activities. The individual laboratory should also be able to decide how many and which QIs can be adopted. In conclusion, the MQI project is proving to be an important tool that, besides providing the TTP error rate and spreading the importance of the use of QIs in enhancing patient safety, highlights critical aspects compromising the widespread and appropriate use of QIs.

Time and Accuracy Measures for Alternative Multidimensional Scaling Data Collection Methods

1975 ◽  
Vol 12 (2) ◽  
pp. 165-170 ◽  
Author(s):  
Walter A. Henry ◽  
Robert V. Stumpf
Keyword(s):  
Data Collection ◽  
Empirical Study ◽  
Multidimensional Scaling ◽  
Response Time ◽  
Response Accuracy ◽  
Data Collection Methods ◽  
Set Size ◽  
Accuracy Measures ◽  
The Impact ◽  
Collection Methods

The impact of stimulus set size and data collection methods on response accuracy and response time were investigated in an empirical study. Anchor Point collection techniques proved superior in reducing respondent time with no sacrifice in accuracy.

scholarly journals Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Inga Gruß ◽  
Arwen Bunce ◽  
James Davis ◽  
Rachel Gold
Keyword(s):  
Data Collection ◽  
Qualitative Data ◽  
Implementation Process ◽  
Process Data ◽  
Data Collection Methods ◽  
Implementation Processes ◽  
The Impact ◽  
Collection Methods ◽  
Qualitative Data Collection

Abstract Background Qualitative data are crucial for capturing implementation processes, and thus necessary for understanding implementation trial outcomes. Typical methods for capturing such data include observations, focus groups, and interviews. Yet little consideration has been given to how such methods create interactions between researchers and study participants, which may affect participants’ engagement, and thus implementation activities and study outcomes. In the context of a clinical trial, we assessed whether and how ongoing telephone check-ins to collect data about implementation activities impacted the quality of collected data, and participants’ engagement in study activities. Methods Researchers conducted regular phone check-ins with clinic staff serving as implementers in an implementation study. Approximately 1 year into this trial, 19 of these study implementers were queried about the impact of these calls on study engagement and implementation activities. The two researchers who collected implementation process data through phone check-ins with the study implementers were also interviewed about their perceptions of the impact of the check-ins. Results Study implementers’ assessment of the check-ins’ impact fell into three categories: (1) the check-ins had no effect on implementation activities, (2) the check-ins served as a reminder about study participation (without relating a clear impact on implementation activities), and (3) the check-ins caused changes in implementation activities. The researchers similarly perceived that the phone check-ins served as reminders and encouraged some implementers’ engagement in implementation activities; their ongoing nature also created personal connections with study implementers that may have impacted implementation activities. Among some study implementers, anticipation of the check-in calls also improved their ability to recount implementation activities and positively affected quality of the data collected. Conclusion These results illustrate the potential impact of qualitative data collection on implementation activities during implementation science trials. Mitigating such effects may prove challenging, but acknowledging these consequences—or even embracing them, perhaps by designing data collection methods as implementation strategies—could enhance scientific rigor. This work is presented to stimulate debate about the complexities involved in capturing data on implementation processes using common qualitative data collection methods. Trial registration ClinicalTrials.gov, NCT02325531. Registered 15 December 2014.

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