scholarly journals Effect of Closed Loop Medication Administration on Drug Returns in Inpatient Facilities

2020 ◽  
Vol 8 (12) ◽  
Author(s):  
Eceberil Ozturk ◽  
Ilker Kose ◽  
Beytiye Elmas
Keyword(s):  
Patient Safety ◽  
Data Quality ◽  
Closed Loop ◽  
Medication Management ◽  
Medication Administration ◽  
Data Set ◽  
Return Rates ◽  
Conventional Drug ◽  
Turkish State

Medication management in inpatient facilities is a crucial issue for patient safety. In inpatient conventional drug management, a common problem relates to drugs prescribed and delivered to patients being returned to the pharmacy without reason for the return. When reasons are given, they are not often regularly and correctly recorded. Closed Loop Medication Administration (CLMA) protects patient safety by managing all processes, including intake of the drug to the hospital's stock, administering the drug to the patient, and disposal of unused drugs using technology. CLMA is known to contribute positively to patient safety. However, there is no study on the effect of CLMA on the return of non-administered drugs. This study aims to analyze the effect of CLMA on drug return rates and investigate the data quality of reasons for drug returns. The research was carried out in three inpatient clinics of a Turkish state hospital (Bolu İzzet Baysal Public Hospital) where the CLMA was implemented in May of 2017. The data set obtained from the hospital information system (HIS) is anonymized. The study showed a significant increase in drug return rates after CLMA, and the data quality of drug return reasons is also significantly improved. These results show that CLMA contributes positively to drug return rates and the data quality of drug return reason records.

Comparing the coding of complications in Queensland and Victorian admitted patient data

2011 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Jude L. Michel ◽  
Diana Cheng ◽  
Terri J. Jackson
Keyword(s):  
Patient Safety ◽  
Data Quality ◽  
Secondary Analysis ◽  
Routine Data ◽  
Complication Rates ◽  
Hospital Data ◽  
Patient Safety Indicators ◽  
Hospital Acquired Conditions ◽  
Hospital Acquired

Objective. To examine differences between Queensland and Victorian coding of hospital-acquired conditions and suggest ways to improve the usefulness of these data in the monitoring of patient safety events. Design. Secondary analysis of admitted patient episode data collected in Queensland and Victoria. Methods. Comparison of depth of coding, and patterns in the coding of ten commonly coded complications of five elective procedures. Results. Comparison of the mean complication codes assigned per episode revealed Victoria assigns more valid codes than Queensland for all procedures, with the difference between the states being significantly different in all cases. The proportion of the codes flagged as complications was consistently lower for Queensland when comparing 10 common complications for each of the five selected elective procedures. The estimated complication rates for the five procedures showed Victoria to have an apparently higher complication rate than Queensland for 35 of the 50 complications examined. Conclusion. Our findings demonstrate that the coding of complications is more comprehensive in Victoria than in Queensland. It is known that inconsistencies exist between states in routine hospital data quality. Comparative use of patient safety indicators should be viewed with caution until standards are improved across Australia. More exploration of data quality issues is needed to identify areas for improvement. What is known about the topic? Routine data are low cost, accessible and timely but the quality is often questioned. This deters researchers and clinicians from using the data to monitor aspects of quality improvement. Previous studies have reported on the quality of diagnosis coding in Australia but not specifically on the quality of use of the condition-onset flag denoting hospital-acquired conditions. What does this paper add? Few studies have tested the consistency of the data between Australian states. No previous studies have evaluated the comprehensiveness of the coding of hospital-acquired conditions using routine data. This paper compares two states to highlight the differences in the coding of complications, with the aim of improving routine data to support patient safety. What are the implications for practitioners? The results imply more work needs to be done to improve the coding and flagging of complications so the data are valid and comprehensive. Further research should identify problem areas responsible for differences in the data so that training and audit strategies can be developed to improve the collection of this information. Practitioners may then be more confident in using routine coded inpatient data as part of the process of monitoring patient safety.

scholarly journals Analysis of After-Hours Patient Telephone Calls in Two Academic Radiation Oncology Departments: An Opportunity for Improvement in Patient Safety and Quality of Care

2016 ◽  
Vol 12 (4) ◽  
pp. e487-e494 ◽  
Author(s):  
Laura E.G. Warren ◽  
Miranda B. Kim ◽  
Neil E. Martin ◽  
Helen A. Shih
Keyword(s):  
Patient Safety ◽  
Quality Of Care ◽  
Adverse Effects ◽  
Radiation Oncology ◽  
Medication Management ◽  
Primary Radiation ◽  
Radiation Oncologist ◽  
After Hours ◽  
Telephone Calls

Purpose: Patient care within radiation oncology extends beyond the clinic or treatment hours. The on-call radiation oncologist is often not a patient’s primary radiation oncologist, introducing the possibility of communication breakdowns and medical errors. This study analyzed after-hours telephone calls to identify opportunities for improved patient safety and quality of care. Methods and Materials: Patient calls received outside of business hours between July 1, 2013, and June 30, 2014, at two academic radiation oncology departments were retrospectively reviewed. All calls were analyzed using content analysis, and descriptive analyses were performed. Results: During this time, 5,557 courses of radiotherapy (RT) were delivered. A total of 454 calls were received from 369 unique patients (81%), averaging 4.4 calls per week per department. Phone encounters were documented for 223 calls (49%). The calls were categorized by disease site (No., %): central nervous system (91, 20%), head and neck (78, 17%), genitourinary (53, 12%), GI (52, 12%), thoracic (51, 11%), gynecologic (30, 7%), breast (24, 5%), and other (75, 17%). Patients most often called regarding acute medical, non–RT-related issues (144 calls, 32%); acute RT-related adverse effects (127, 28%); and medication management, including refills (63, 14%). Conclusion: This analysis provided novel information regarding the volume of and reasons for after-hours patient-initiated telephone calls. It identified opportunities for actionable improvements in safety and quality of care, particularly with regard to documentation by on-call providers, communication with the primary radiation oncology and extended health care teams, patient education about common RT adverse effects, and medication management.

scholarly journals Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weijin Jiang ◽  
Junpeng Chen ◽  
Xiaoliang Liu ◽  
Yuehua Liu ◽  
Sijian Lv
Keyword(s):  
Data Quality ◽  
Distance Constraint ◽  
Mobile Crowd Sensing ◽  
Constraint Factor ◽  
Data Set ◽  
Selection Scheme ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Mobile Crowd

With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.

Keselamatan Pemberian Medikasi

2010 ◽  
Vol 13 (2) ◽  
pp. 105-111
Author(s):  
Agustin Indracahyani
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Medication Errors ◽  
Health Care Services ◽  
Medication Management ◽  
Medication Administration ◽  
Process Approach ◽  
Nursing Process ◽  
Care Services ◽  
The World

AbstrakKesalahan medikasi merupakan masalah yang sangat serius di pelayanan kesehatan di seluruh dunia. Masalah tersebut mengakibatkan cedera dan kematian bagi pasien, serta meningkatkan biaya yang harus dikeluarkan oleh rumah sakit. Kesalahan medikasi dapat terjadi di setiap tahapan proses manajemen dan penggunaan medikasi dan berakibat pada keselamatan pasien. Kesalahan medikasi dapat terjadi akibat kondisi laten, kondisi yang menyebabkan kesalahan, dan kegagalan aktif. Perawat sebagai pihak yang paling banyak terlibat dalam proses pemberian medikasi memiliki peran penting dalam mencegah, mengenali, dan mengatasi terjadinya kesalahan untuk meningkatkan keselamatan pemberian medikasi. Upaya meningkatkan keselamatan pemberian medikasi dilakukan melalui pendekatan proses keperawatan sejak pengkajian hingga evaluasi dan dokumentasi. AbstractMedication errors are a very serious problem in health care services around the world. These problems lead to morbidity and mortality for patients, as well as increase the costs to be incurred by the hospital. Medication errors may occur at any stages of medication management and use process and result in patient safety. These may occur due to latent conditions, error producing conditions, and active failures. Nurses who are primarily involved in the process of medication administration have important role in preventing, recognizing, and addressing errors in order to enhance safety medication administration. Efforts to enhance safety medication administration may be done through nursing process approach from assessment to evaluation and documentation.

scholarly journals Medication Safety Practices: A Patient's Perspective

2016 ◽  
Vol 8 (4) ◽  
pp. 271-281
Author(s):  
Sunita Ghike ◽  
Krutika Bhalerao ◽  
Anuja V Bhalerao
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Quality Of Health Care ◽  
Medication Safety ◽  
Medication Administration ◽  
Obstetrics And Gynecology ◽  
Sample Population ◽  
Safety Practices ◽  
The Mean

ABSTRACT Introduction Medication administration constitutes a key element of acute care delivery, while errors in the process threaten patient safety. A foundational cornerstone upon which health care providers endeavor to base all care is the medical oath, “Never do harm to anyone” (Hippocrates). Medication use in hospitals is a complex process and depends on successful interaction among health care personnel functioning at different areas, and errors may occur at any stage of prescribing, documenting, dispensing, preparation, or administration. The purpose of this research is to explore the safety practices employed by nurses during medication administration, specifically from the patients’ perspectives. The fundamental objectives are to explore patients’ perceptions, attitudes, and beliefs about the safety practices utilized by nurses when administering medications and to identify opportunities for increasing patient safety. Materials and methods This study was undertaken employing a quantitative survey instrument as the methodology. For collecting data, a pretested, structured questionnaire was given to the sample population after fulfilling the inclusion/exclusion criteria, and consent to enroll in study was taken. This method is convenient and affords the opportunity to generalize responses from the sample population to the population as a whole. Results The mean age of the respondents from Obstetrics and Gynecology was 29.08 ± 6.53. The mean age of the respondents from medicine was 33.4 ± 9.6. The mean age of the respondents from surgery was 33.68 ± 12.2; 23% respondents belonged to medicine unit, 21.5% respondents belonged to surgery unit, and 55.5% respondents belonged to Obstetrics and Gynecology unit. Of the total respondents, 62.75% were females. Moreover, 76 respondents in medicine, 72 respondents in surgery, and 172 respondents in Obstetrics and Gynecology stayed in the hospital for > 7 days. Respondents < 30 years of age responded negatively to three out of six questions compared to respondents > 30 years of age. This is statistically significant (p = 0.008, 0.0001, and 0.008) showing that age does not alter the perception of the quality of health care. The perception of medicine respondents was negative to four out of six questions as compared to surgery respondents. This is statistically significant (p = 0.008, 0.0001, and 0.008), thus unit alters the perception of the quality of health care. The medicine respondents rated care lower as compared to surgical respondents. The patients’ perception varies with gender, and it has been found to be significant in five out of six cases where p value is < 0.05. Females rated the quality of care better and shared the responsibility for health care. The patients’ perception varies with length of stay (LOS): 47.25% respondents feel that the nursing care of the hospital is very safe; 63% respondents feel that their care is a responsibility shared by both doctors as well as themselves more so by the female respondents from surgical units and who stayed longer. Conclusion According to patients’ perceptions reported in this study, there were a number of inconsistencies noted in the seven rights of medication administration delivered by nurses, specifically patient identification, hand washing, allergy assessment, and patient teaching. The perception of medication safety practices do change with the unit they are in, gender, and LOS. The results identify key safety issues from a patients’ perspective to focus change strategies that will improve patient care. How to cite this article Bhalerao K, Ghike S, Bhalerao AV. Medication Safety Practices: A Patient's Perspective. J South Asian Feder Obst Gynae 2016;8(4):271-281.

scholarly journals Importance of Continued Data Quality Assessment of Syndromic Production Data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Sophia Crossen
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Production Data ◽  
Quality Of Data ◽  
Data Set ◽  
Quality Degradation ◽  
Periodic Data ◽  
Data Quality Assessment

ObjectiveTo explore the quality of data submitted once a facility is movedinto an ongoing submission status and address the importance ofcontinuing data quality assessments.IntroductionOnce a facility meets data quality standards and is approved forproduction, an assumption is made that the quality of data receivedremains at the same level. When looking at production data qualityreports from various states generated using a SAS data qualityprogram, a need for production data quality assessment was identified.By implementing a periodic data quality update on all productionfacilities, data quality has improved for production data as a whole andfor individual facility data. Through this activity several root causesof data quality degradation have been identified, allowing processesto be implemented in order to mitigate impact on data quality.MethodsMany jurisdictions work with facilities during the onboardingprocess to improve data quality. Once a certain level of data qualityis achieved, the facility is moved into production. At this point thejurisdiction generally assumes that the quality of the data beingsubmitted will remain fairly constant. To check this assumption inKansas, a SAS Production Report program was developed specificallyto look at production data quality.A legacy data set is downloaded from BioSense production serversby Earliest Date in order to capture all records for visits which occurredwithin a specified time frame. This data set is then run through a SASdata quality program which checks specific fields for completenessand validity and prints a report on counts and percentages of null andinvalid values, outdated records, and timeliness of record submission,as well as examples of records from visits containing these errors.A report is created for the state as a whole, each facility, EHR vendor,and HIE sending data to the production servers, with examplesprovided only by facility. The facility, vendor, and HIE reportsinclude state percentages of errors for comparison.The Production Report was initially run on Kansas data for thefirst quarter of 2016 followed by consultations with facilities on thefindings. Monthly checks were made of data quality before and afterfacilities implemented changes. An examination of Kansas’ resultsshowed a marked decrease in data quality for many facilities. Everyfacility had at least one area in need of improvement.The data quality reports and examples were sent to every facilitysending production data during the first quarter attached to an emailrequesting a 30-60 minute call with each to go over the report. Thiscall was deemed crucial to the process since it had been over a year,and in a few cases over two years, since some of the facilities hadlooked at data quality and would need a review of the findings andall requirements, new and old. Ultimately, over half of all productionfacilities scheduled a follow-up call.While some facilities expressed some degree of trepidation, mostfacilities were open to revisiting data quality and to making requestedimprovements. Reasons for data quality degradation included updatesto EHR products, change of EHR product, work flow issues, engineupdates, new requirements, and personnel turnover.A request was made of other jurisdictions (including Arizona,Nevada, and Illinois) to look at their production data using the sameprogram and compare quality. Data was pulled for at least one weekof July 2016 by Earliest Date.ResultsMonthly reports have been run on Kansas Production data bothbefore and after the consultation meetings which indicate a markedimprovement in both completeness of required fields and validityof values in those fields. Data for these monthly reports was againselected by Earliest Date.ConclusionsIn order to ensure production data continues to be of value forsyndromic surveillance purposes, periodic data quality assessmentsshould continue after a facility reaches ongoing submission status.Alterations in process include a review of production data at leasttwice per year with a follow up data review one month later to confirmadjustments have been correctly implemented.

scholarly journals Validity as a Measure of Data Quality in Internet of Things Systems

2021 ◽  
Author(s):  
Rishabh Deo Pandey ◽  
Itu Snigdh
Keyword(s):  
Data Quality ◽  
Data Sets ◽  
Major Focus ◽  
Quality Of Data ◽  
Data Set ◽  
Environment Analysis ◽  
Aggregated Data ◽  
Pervasive Environment ◽  
Validation Parameters

Abstract Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis.

Global indicators of X-ray data quality

2001 ◽  
Vol 34 (2) ◽  
pp. 130-135 ◽  
Author(s):  
Manfred S. Weiss
Keyword(s):  
Data Quality ◽  
Diffraction Data ◽  
Optical Resolution ◽  
Data Set ◽  
X Ray ◽  
Global Indicators ◽  
Nominal Resolution

Global indicators of the quality of diffraction data are presented and discussed, and are evaluated in terms of their performance with respect to various tasks. Based on the results obtained, it is suggested that some of the conventional indicators still in use in the crystallographic community should be abandoned, such as the nominal resolutiondminor the mergingRfactorRmerge, and replaced by more objective and more meaningful numbers, such as the effective optical resolutiondeff,optand the redundancy-independent mergingRfactorRr.i.m.. Furthermore, it is recommended that the precision-indicating mergingRfactorRp.i.m.should be reported with every diffraction data set published, because it describes the precision of the averaged measurements, which are the quantities normally used in crystallography as observables.

scholarly journals An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Ali S Afshar ◽  
Yijun Li ◽  
Zixu Chen ◽  
Yuxuan Chen ◽  
Jae Hun Lee ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intensive Care Unit ◽  
Heart Rate ◽  
Time Series ◽  
Intensive Care ◽  
Data Quality ◽  
Vital Signs ◽  
Data Set ◽  
Mimic Iii

Abstract Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.

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