Entry Errors Research Articles

Abstract C004: Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study

Abstract Background: The CaPTC prostate cancer (CaP) Familial Cohort study (CaPFCS) was launched in 2017 to explore the CaP risk factors of African Black men (ABM). The study design is a prospective study that observes and follows healthy ABM and ABM diagnosed with CaP. Aim: The specific aim is to provide recommendations on statistical considerations for cohort studies focused on cancer disparities, based on lessons learned from the CaPTC CaPFCS. Methodology: Study participants were ABM in Cameroon, Nigeria, and the US. Using validated measures, data were collected mostly by self-administered or research assistant administered survey using paper survey (especially in Cameroon and Nigeria) with the responses transferred to REDCap by research coordinators. The statistical analysis for the CaPTC CaPFCS focused on the baseline epidemiological, behavioral, clinical and anthropometric measures of Black men. A total of 819 participants were included in the analyses. Statistical analyses included descriptive statistics (sample median, range, frequency and percentages) and unadjusted logistic regression models. Results: Data collection for the CaPTC CaPFCS were by multiple investigators/research coordinators at multiple sites in Cameroon, Nigeria, and the US. In preparation for the data analyses, we noted data entry errors and/or misinterpretation of data entry, which required extensive cleaning of the database prior to the analyses. The biostatisticians spent significant effort cleaning and ensuring data validity prior to the analyses. Confounding variables is another statistical consideration for cohort studies, such as the CaPTC CaPFCS. There was variability observed across age, education levels, employment status, household income and CaP diagnosis based on country of residence. CaP diagnosis was not reported by any of the ABM in the US, which may be due to the fact that they were all recruited within the community and no recruitment took place in the clinic. Finally, the CaPTC CaPFCS has several variables. Performing a lot of statistical tests may result in false-positive findings at P<0.05. Conclusion: The advantage of the CaPFCS is that it allows the CaPTC investigators to study epidemiological, behavioral, clinical and anthropometric variables associated with CaP over time. Being a longitudinal cohort study, investigators can track the changes in exposure and outcomes over time.However, statistical considerations need to be at the fore-front of multi-center, transatlantic cancer disparity studies such as the CaPTC CaPFCS, from data collection to data entry. The use of web-based survey with the ability to enter data directly into REDCaP will significantly reduce (if not totally eliminate) the need for database cleaning. There are multiple strategies that can be used to reduce confounder effect, including statistical adjustment and multivariate analysis to adjust for the confounders. The use of propensity score methods have especially been found to be effective in controlling baseline confounding factors. Citation Format: Daniel Lee, Michael Heckman, Zhongwei Peng, Emeka Iweala, Ademola Popoola, Paul Jibrin, Mohammed Faruk, Anthonia Sowumi, Omolara Fatiregun, Nkegoum Blaise, Catherine Oladoyinbo, Ifeoma Okoye, Abdulkadir Ayo Salako, Abidemi Omonisi, Iya Eze Bassey, Kayode Adeniji, Nggada Haruna Asura, Ernest Kaninjing, Oluwole Kukoyi, Fathi Parisa, Ruth Enuka, Oluwaseyi Toye, Jennifer Crook, CaPTC Investigators, Folakemi Odedina. Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C004.

ANALYSIS OF EMPLOYEE PAYROLL INTERNAL CONTROL SYSTEM AT PT. ZIKRUL JUDGE BESTARI SURABAYA BRANCH

This study aims to determine the payroll system and internal control of the payroll system at PT. Zikrul Hakim Bestari Surabaya Branch. This study uses a qualitative descriptive research method. Data were collected by interview and documentation methods. The results showed that the payroll system carried out by PT. Zikrul Hakim Bestari Surabaya Branch has gone through the system: AppSheet, Sisfo Audit and Payroll. The head office in Ciawi, Bogor, carries out payroll. The only function that is applied is the payroll function and the financial function; even then, it is carried out by the same person. Documents used in the payroll process are employee attendance lists and salary slips. Human errors occur in the company's payroll process (data entry errors). Internal control of payroll at PT. Zikrul Hakim Bestari Surabaya Branch has done well, although not optimally. This can be seen from the dual tasks that occur in the company.

Evaluating automated electronic case report form data entry from electronic health records.

Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety. We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance). The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error. An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.

HPB P46 Does the CholeS pre-operative risk score for laparoscopic cholecystectomy correlate to operative time? A single centre experience

Abstract Background NHS waiting lists for elective surgeries including non-urgent laparoscopic cholecystectomies (LC) have severely escalated during the Covid-19 pandemic, with some patients waiting over 2 years for their operations. LCs are highly variable in terms of operative time, difficulty, and risk making theatre utilisation a challenge to effectively clear waiting lists. Nassar et al.[1] developed and validated a pre-operative risk prediction score for predicting the difficult LC using an objective operative difficulty grading system. We aimed to assess if application of CholeS could be used to predict which LC may have longer operative times, and so aid theatre utilisation planning. Methods Consecutive elective LCs performed between May and October 2021 at our institution's day surgery unit were included and analysed. Each patient was scored retrospectively using the CholeS pre-operative risk score from electronic patient records. Operative time was obtained from theatre electronic record systems. Data was collected on conversion to open surgery, post-operative day of discharge, and intra or post-operative complications. Outliers with operating time recorded as <20 minutes were considered data entry errors and excluded from analysis. A ROC analysis was used, which determined a threshold value of 3. This value was used to divide patients into a low-risk (≤3 points) and high-risk (four and above) group. Two-sample independent t-test was used to compare mean operative time between the high-risk and low-risk CholeS score groups. Levene's test was used to determine if variance was equal between groups. SPSS version 27[2] was used for data analysis and statistical tests and p<0.05 was deemed significant. Results 81 LC were included for analysis. 53 patients were low-risk and 26 patients were high-risk. There was a significantly lower operative time in the low-risk group: low-risk = 57.6 minutes (95% CI 52.4–63.0) vs high-risk = 75.8 minutes (95% CI 58.7–92.9), p=0.046. Nine patients had surgeries lasting >90 minutes; 66% of these were in the high-risk LC group. 95% of patients were discharged on day 0, two patients on day 1, and two on day 2 or later. Three patients had conversion to open cholecystectomy and five patients had post-operative complications. Two out of three patients who required conversion to open cholecystectomies were in high-risk patients with high CholeS scores (7 and 10). In one patient, a cholecystoduodenal fistula was found. Cystic duct avulsion occurred in the other. Three out of five patients with post-operative complications were in the high-risk group, with corresponding higher Clavien-Dindo scores (3b, 2 and 2) when compared to the low-risk group (1 for both patients). Conclusions The CholeS pre-operative scoring system could be used to optimise LC theatre allocation. A score of ≤3 has a shorter operative time than a patient with a score of four or more (mean difference = 18.1 mins, 95% CI 4.4–31.9). Prediction of which LCs will be shorter operations could improve theatre utilisation and allow extra cases booking on the operating list. This, in turn, could help reduce the number of patients on the waiting list. Additionally, CholeS could be used to predict patients with more challenging and prolonged operations as well as those at higher risk of open conversion and complications. This could allow such patients to be managed by allocation to inpatient specialist upper GI specialist lists. [1] Nassar, A.H.M., Hodson, J., Ng, H.J. et al. Predicting the difficult laparoscopic cholecystectomy: development and validation of a pre-operative risk score using an objective operative difficulty grading system. Surg Endosc 34, 4549–4561 (2020). [2] IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.

RWD70 Electronic Implementation of Patient-Reported Outcomes: Challenges and Potential Solutions

As a consequence of recent advances in information technology and wider availability of personal portable devices, the use of electronic Patient-Reported Outcomes (ePROs) in clinical research is constantly growing. Although ePROs have several advantages (such as limited data entry errors and ease of remote compilation), their employment poses new challenges both when reproducing paper-based PROs onto electronic platforms, and when it is necessary to transfer an already available ePRO from the original implementation platform to a different data collection system. The aim of this work is to report challenges and solutions when implementing ePROs.

The Urgence of Using the Online Village Financial System (Siskeudes) in Achieve the Accountability of Village Financial Management during the COVID-19

This article discusses the online version of the Village Financial System (Siskeudes) application in realizing village financial management accountability during the Covid-19 Pandemic. Siskeudes is an application built based on the Regulation of the Minister of Home Affairs of the Republic of Indonesia Number 20 of 18 concerning Village Financial Management. Siskeudes is intended for village governments to make it easier to manage village finances starting from the planning, budgeting, to reporting and accountability stages. The advantages of the Siskeudes application according to the results of research by experts include, first, Siskeudes significantly supports the achievement of the objectives of an accounting information system (AIS), which is to produce fast, precise, and accurate information. Second, Siskeudes benefits users in village, sub-district, and district/city governments in managing village finances to be more efficient, accountable, and safe. In addition, there are several obstacles in the use of Siskeudes including, first, the competence of human resources is still lacking. Second, there is still a lack of supporting facilities. Third, it is still difficult to find errors in data entry. Fourth, errors often occur when entering data on Siskeudes online. Apart from the advantages and disadvantages of using the Siskeudes application, there are benefits to using the Siskeudes application during the Covid-19 Pandemic, including avoiding crowds. Second, prevent new clusters of positive cases of Covid-19. Third, continue to maintain physical distancing with fellow officers and the public. Fourth, Support the Policy for the Implementation of the Java and Bali Micro-Scale Community Activity Restrictions (PPKM) which has been in effect since January 11, 2021.

Implications of Current Procedural Terminology code accuracy on surgical workflow and financial reimbursement

Objective: Inaccuracies in Current Procedural Terminology (CPT) coding entries for surgical procedures have a profound impact on hospital systems and surgeon compensation for services. We sought to characterize the variations of surgical CPT entry at a multi-site academic medical center and estimate the financial burden implicated by improper code entry.Methods: A mixed methods study was conducted to evaluate variations in CPT entry across an academic center. Semi-structured interviews with 8 surgical schedulers were conducted and analyzed to understand the current scheduling process. Coding data for surgical procedures performed within a 31-day period during September and October 2020 within the large healthcare system were assessed for appropriate CPT code entry. Reimbursement for the 2020 fiscal year was then analyzed to determine the impact of pre-operative CPT code accuracy on reimbursements and denials.Results: Interviews revealed a lack of standardization in the surgical scheduling process across the hospital system. Lack of standardized onboarding and variations in workflow contributed to difficult cross coverage for schedulers and errors in CPT entry. On quantitative analysis, the accuracy of pre-operative CPT code entry was poor with only 59.3% of pre-operative CPT code entries being correct. In the 2020 fiscal year, $5.4 million was lost due to problems related to CPT code entry.Conclusions: Variations and lack of standardization in CPT code entry can greatly contribute to financial losses and disrupt surgical scheduling. Standardization of workflow and CPT entry schemes can help minimize scheduling complications and enhance the quality of care provided to patients.

Machine learning analysis to identify data entry errors in prehospital patient care reports: a case study of a national out-of-hospital cardiac arrest registry

Background: To develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as “absent” in the coded data but “present” in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11% and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of EMS clincians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.

The influence of physician-nurse collaboration on patient safety culture

BackgroundMany factors in health care organizations affect patients' safety, such as; multi-professional teamwork and collaboration. Physician-Nurse collaboration is the most crucial inter-professional collaboration in the health care system. Because these professionals are the closest to the patient and make most decisions related to patients' conditions, Physician-Nurse collaboration affects patients' health condition consequences, length of stay in health care facilities, patients' mortality, and the existence of medical errors. Aim of the studyThis study explores the relationship between physician-nurse collaboration and patient safety culture and compares patient safety culture levels between Jordanian hospitals from different sectors. In addition, examine differences in patient safety culture levels according to the position of health care providers (i.e., nurse managers, RN, and physicians). MethodsA descriptive, cross-sectional design using a self-administered questionnaire was used for the current study. Data were collected between February and May of 2019. Four different hospital settings in Jordan (University, not-for-profit, private and governmental hospitals) were selected. In addition, we recruited a convenience sample representing registered nurses, nurse managers, and physicians at the selected hospitals. MeasurementsThree self-administered questionnaires were used to collect data for the current study: Demographic Data, Collaboration and Satisfaction About Care Decisions (CSACD), and Hospital Survey on Patient Safety Culture version 1.0 (HSOPS). Data analysisData were screened for errors in data entry, outliers, or missing values. Data were normally distributed without extreme outliers. This study used descriptive statistics, the Pearson product-moment correlation, one-way ANOVA, and the Chi-square tests were used in this study. The level of significance (alpha value) is set at 0.05. Resultsshowed that physician-nurse collaboration had a significant positive relationship with all patient safety culture levels (P < 0.01). In addition, the Pearson's product-moment correlation coefficient results indicated that all patient safety culture scores and subscales were positively and significantly correlated with physician-nurse collaboration (P < 0.01). Furthermore, the results of one-way ANOVA showed a statistically significant difference in the overall perception of patient safety culture according to the position of participants (P < 0.01). Moreover, Participants in Not-for-Profit Hospitals were more likely to report an ‘excellent/very good’ patient safety grade (P < 0.001) than in other hospitals. ConclusionPhysician-nurse collaboration positively impacts overall patient safety culture grades. Health care organization in Jordan has the potential to increase levels of patient safety cultures; however, to achieve this aim, there should be a stronger focus on building effective inter-professional collaboration and building a blame-free culture among healthcare providers, and these organizations should receive the needed support from health care leaders in the country. To help strengthen the health care system, raise patient safety culture levels, and improve quality.

PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care

BackgroundClinical phenotype information greatly facilitates genetic diagnostic interpretations pipelines in disease. While post-hoc extraction using natural language processing on unstructured clinical notes continues to improve, there is a need to improve point-of-care collection of patient phenotypes. Therefore, we developed “PheNominal”, a point-of-care web application, embedded within Epic electronic health record (EHR) workflows, to permit capture of standardized phenotype data.MethodsUsing bi-directional web services available within commercial EHRs, we developed a lightweight web application that allows users to rapidly browse and identify relevant terms from the Human Phenotype Ontology (HPO). Selected terms are saved discretely within the patient’s EHR, permitting reuse both in clinical notes as well as in downstream diagnostic and research pipelines.ResultsIn the 16 months since implementation, PheNominal was used to capture discrete phenotype data for over 1500 individuals and 11,000 HPO terms during clinic and inpatient encounters for a genetic diagnostic consultation service within a quaternary-care pediatric academic medical center. An average of 7 HPO terms were captured per patient. Compared to a manual workflow, the average time to enter terms for a patient was reduced from 15 to 5 min per patient, and there were fewer annotation errors.ConclusionsModern EHRs support integration of external applications using application programming interfaces. We describe a practical application of these interfaces to facilitate deep phenotype capture in a discrete, structured format within a busy clinical workflow. Future versions will include a vendor-agnostic implementation using FHIR. We describe pilot efforts to integrate structured phenotyping through controlled dictionaries into diagnostic and research pipelines, reducing manual effort for phenotype documentation and reducing errors in data entry.

Where Is Research in the Era of Electronic Health Records?

IntroductionData extraction from electronic health records (EHRs) for use in clinical research continues to be labor-intensive and to offer little benefit over traditional paper chart reviews. This is largely due to poor integration of EHR systems with hospital process flow, which still relies heavily on traditional paperwork as a means of documentation. DiscussionNew methods in data collection through mobile applications have streamlined data entry through better data standardization and improved overall data quality. However, mobile applications address only a portion of the problem. Data entry errors and legacy integration will continue to be an issue when there are changes between practitioners with different EHR systems. The combination of a mobile application with the cloud platform has been applied in multiple specialties to monitor recovery and patient-reported outcomes. ConclusionMobile applications along with a virtual cloud environment to host data provide a reasonable solution for consolidating patient data and can accelerate population research.

O-123 FMEA analysis of an automatic integration of time-lapse incubators into electronic medical records using CHLOE(Fairtility) shows risk reduction through automation of data capture and processing

Abstract Study question Can integrations and automatic data processing between time-lapse incubators and EMRs reduce the risks associated with manual moving of data from time-lapse incubators to EMRs Summary answer Redesigning data workflow using CHLOE(Fairtility) decreased risk occurrence and increased risk detection possibilities associated with embryo classification and selection to freeze, biopsy, transfer and discard. What is known already Decisions are made from information derived from time-lapse incubators. Clinically, embryologists decide which embryos (and when) are suitable for transfer, cryopreservation, biopsy, or discarding based on data derived from time-lapse incubators, manually annotated and summarised into the electronic medical record (EMR) where further information useful for embryo selection is stored. Manual movement of data from time-lapse incubators to EMRs is time-consuming, administrative, reduces the granularity of the data available and incurs risk of human-error inaccuracies. These challenges limit the possibilities of how this data can be used to optimise clinical decisions, improve the patient experience and proactively detect operational anomalies. Study design, size, duration Failure mode effects analysis (FMEA) analysis was carried out on the workflow integration into a large (&amp;gt;5000 cycles per annum) IVF centre following ESHRE guidelines for laboratory and time-lapse practice (ESHRE,2015,2020), comparing before and after the introduction of CHLOE(Fairtility). The FMEA analysis evaluated the possible data capture, processing and associated clinical decision risks from embryos entering to leaving the time-lapse incubator. The Risk Priority Number (RPN=likelihoodxseverityxdetection of incidence) was calculated for each failure mode (Rienzi,2015). Participants/materials, setting, methods Through authenticated REST API calls according to the OpenAPI standard, CHLOE(Fairtility) linked the treatment unique identifier from the EMR(LIVO, inhouse developed) to the time-lapse incubator, automatically processed the time-lapse data, captured quantitative and qualitative information (such as morphokinetic time points, PNs, cleavage and blastocyst morphological grades, unusual embryo developmental anomalies and prediction scores for blastulation and implantation) and automatically loaded into the EMR. Main results and the role of chance Before CHLOE(Fairtility), 8 process phases were identified, with 81 associated failure modes, among which 45 risks were given a moderate RPN [RPN&amp;gt;15, i.e. data entry error into the EMR; image feature detection missed (i.e. 2PNs, Inner Cell Mass, incorrectly diagnosing fragments as cells and vice versa; incorrectly diagnosing vacuoles as a PNs, asynchronous PNs missed),with consequences including inaccurate KPI monitoring (n = 20, RPN=4); reduced patient experience and increased stress (n = 18, RPN range 3-16); wrong embryo being selected (n = 42, RPN range 8-36). Wrong embryo selection had three possible consequences: viable embryo discarded leading to a reduction in efficacy of treatment; viable embryo not prioritised for transfer causing reduced chance of pregnancy, or increased time to pregnancy, increasing cost and emotional burden); euploid embryo not prioritised for biopsy, increasing cost. Overall, RPN ranged from 3 to 36. After CHLOE(Fairtility), 51 failure modes were eliminated completely, including quantitative and qualitative morphokinetic annotations, entering data into the EMR for daily embryo grades, and embryo fate decisions. A further 22 failure modes had reduced RPN, including blastocyst morphological grading, number of PNs, identification of unusual embryo cleavages; with 30 low RPNs and 6 moderate RPNs. Implementation of CHLOE(Fairtility) reduced the highest RPN from 36 to 16. Limitations, reasons for caution FMEA is a proactive method to identify potential incidents in order to develop strategies to mitigate risks, forming part of a framework for responsible innovation. The likelihood of incidences were estimated based on a PUBMED literature review, personal experience and the experience of colleagues. Wider implications of the findings CHLOE(Fairtility) has the potential to eliminate risks that exist when manually moving data from time-lapse incubators to EMRs: time-consuming, administrative, reduced data granularity and human-error-based inaccuracies. CHLOE(Fairtility) optimises clinical decisions, providing an opportunity for personalised patient care, improved patient engagement, and the potential to detect operational non-conformities before impacting clinically. Trial registration number NA

ANALISIS SISTEM INFORMASI AKUNTANSI PENGGAJIAN PADA DINAS KOMUNIKASI DAN INFORMATIKA PROVINSI SUMATERA UTARA

In compliance with applicable legislation, salary provision must be supported by evidence in the form of paperwork and other records. Several factors must be considered when determining wage to ensure that the pay is commensurate with the work performed by employees, such as attendance lists, so that employees are more encouraged to adhere to attendance policies. In this situation, the payroll accounting information system makes it easier for accountants to complete computations and keep track of everything relevant to payroll transactions. This payroll information system, on the other hand, can lead to data entry errors. If you enter the erroneous account number, especially while distributing salaries, the money could end up in someone else's account, resulting in significant losses. This study was carried out at the North Sumatra Province's Department of Communication and Information (Diskominfo). By performing direct observations and interviews with numerous employees, this study takes a descriptive qualitative method. The findings of this study show that the payroll system is still ineffective since it does not consider employee attendance hours when calculating employee salary. It would be preferable if attendance hours were factored into salary calculations, as this would encourage employees to be more punctual.

Healthcare resource utilization and costs among patients with psoriasis treated with ixekizumab or adalimumab over 2 years of follow-up in real-world settings

Aims To compare long-term healthcare resource utilization (HCRU) and costs among patients who initiated ixekizumab (IXE) or adalimumab (ADA) for treatment of psoriasis in the United States. Methods Adult patients with psoriasis who had ≥1 claim for IXE or ADA were identified from IBM MarketScan claims databases prior to the COVID-19 pandemic (1 March 2016–31 October 2019). The index date was the date of first claim for the index drug of interest. Inverse probability of treatment weighting was employed to balance treatment cohorts. All-cause and psoriasis-related HCRU and costs were examined for 24 months of follow-up. Costs were reported as per patient per month. Costs of psoriasis-related biologics were adjusted using published Institute for Clinical and Economic Review (ICER) discount factors. Index drug costs were adjusted for adherence and ICER discount rates. Results The analyses included 407 IXE and 2,702 ADA users. IXE users had significantly higher inpatient admission rate (all-cause HCRU: 14.9% vs. 11.0%; p =0.012) and greater mean length of stay per admission (days, 6.6 vs. 4.1; p =0.004) than ADA users. ICER-adjusted costs were significantly higher in IXE than ADA users (all-cause costs: $4,132 vs. $3,610; p <0.001; psoriasis-related costs $3,077 vs. $2,700; p <0.001). After adjusting for ICER and adherence, IXE and ADA drug costs were comparable ($3,636 vs. $3,677; p =0.714). Limitations Study relied on administrative claims data, subjected to data coding limitations and data entry errors. Rebates, patient assistance programs, and commission to wholesalers are not always captured in claims. Adjustment made by ICER discount factors may lead to double-discounting if the discounts have been applied in claim payments. Conclusions All-cause HCRU was higher in IXE than ADA users. Healthcare costs were also higher in IXE than ADA users after ICER adjustment, over 24 months. Cost differences were largely driven by higher treatment adherence associated with IXE. Index drug costs were comparable after ICER and adherence adjustments.

Electronic Health Record-Integrated Tumor Board Application to Save Preparation Time and Reduce Errors.

Multidisciplinary oncology meetings, or tumor boards (TBs), ensure and facilitate communication between specialties regarding the management of cancer cases to improve patient care. The organization of TB and the preparation and presentation of patient cases are typically inefficient processes that require the exchange of patient information via e-mail, the hunting for data and images in the electronic health record, and the copying and pasting of patient data into desktop presentation software. We implemented a standards-based electronic health record-integrated application that automated several aspects of TB organization and preparation. We hypothesized that this application would increase the efficiency of TB preparation, reduce errors in patient entry, and enhance communication with the clinical team. Our experimental design used a prospective evaluation by pathologists who were timed in preparing for weekly TBs using both the new application and the conventional method. In addition, patient data entry errors associated with each method were tracked, and TB attendees completed a survey evaluating satisfaction with the new application. The total time savings for TB preparation using the digital TB application over the conventional method was 5 hours and 19 minutes, representing a 45% reduction in preparation time (P < .01). Survey results showed that 91% of respondents preferred the digital method and believed that it improved the flow of the TB meeting. In addition, most believed that the digital method had an impact on subsequent patient care. This study provides further evidence that new electronic systems have the potential to significantly improve the overall TB paradigm by optimizing and enhancing case organization, preparation, and presentation.

Analysis of Customer Assessment of the Switching and Ease of Reserving Manual Transactions to Digital at Bank Syariah Indonesia KCP Medan Tomang Elok

Advances in technology today make it easy for all transactions. In fact, banking institutions such as Bank Syariah Indonesia are currently looking at the website as a means to apply online transaction reservations. Manual transactions are starting to be abandoned because they are considered less effective at this time. Bank Syariah Indonesia, especially Bank Syariah Indonesia Kcp Medan Tomang Elok has implemented an easy reservation method through the official digital web of Bank Syariah Indonesia. Digital transaction reservations are also considered easier for people of all ages to use. The purpose of this study is (1) to determine customer assessment of the manual to digital reservation method at Bank Syariah Indonesia Kcp Medan Tomang Elok. (2) Knowing the level of convenience in using the official digital web of Bank Syariah Indonesia by customers. To obtain these data, researchers used observation, interviews and documentation as data collection techniques. The data obtained from the research method was processed using qualitative descriptive analysis as the result of the research. Bank Syariah Indonesia Kcp Medan Tomang Elok has implemented the latest innovations to facilitate all customer transactions, so that transactions become faster and more efficient and minimize errors in manual data entry by customers..

Comparison of Implant Placement Accuracy in Healed and Fresh Extraction Sockets between Static and Dynamic Computer-Assisted Implant Surgery Navigation Systems: A Model-Based Evaluation.

The aim of this model-base study was to compare the accuracy of implant placement between static and dynamic computer-assisted implant surgery (CAIS) systems in a fresh extraction socket and healed ridge. A randomized in vitro study was conducted. Twenty 3D-printed maxillary models and 80 implants were used. One experienced researcher placed the implants using either the static navigation or dynamic navigation system. Accuracy was measured by overlaying the real position in the postoperative CBCT on the virtual presurgical placement of the implant in a CBCT image. Descriptive and bivariate analyses of the data were performed. In the fresh sockets, the mean deviation was 1.24 ± 0.26 mm (entry point), 1.69 ± 0.34 mm (apical point), and 3.44 ± 1.06° (angle discrepancy) in the static CAIS group, and 0.60 ± 0.29 mm, 0.78 ± 0.33 mm, and 2.47 ± 1.09° in the dynamic CIAS group, respectively. In the healed ridge, the mean deviation was 1.09 ± 0.17 mm and 1.40 ± 0.30 mm, and 2.12 ± 1.11° in the static CAIS group, and 0.80 ± 0.29 mm, 0.98 ± 0.37 mm, and 1.69 ± 0.76° in the dynamic CIAS group, respectively. Compared with the static CAIS system, the dynamic CAIS system resulted in significantly lower entry and apical errors in both fresh sockets and healed ridges. Differences in bone morphology therefore seem to have little effect on accuracy in the dynamic CAIS group.

A service evaluation of the accuracy of electronic prescriptions used to calculate nebulised medication adherence in adult with cystic fibrosis

Adherence to nebulised medications in people with cystic fibrosis (CF) is known to be suboptimal. CFHealthHub uses an electronic prescription (e-prescription) as a denominator and chipped nebuliser devices which capture the frequency of nebulised medications inhaled by the patient. This enables a calculation of nebulised medication adherence to be made. However, e-prescriptions may contain errors which can affect the adherence calculation. This service evaluation sought to review the accuracy of CFHealthHub e-prescriptions at a single adult CF centre, to understand the nature and causes of any inaccuracies and to evaluate the effect of prescription complexity on prescription accuracy. A total of thirty e-prescriptions from CFHealthHub were compared to ‘gold standard’ prescriptions. Inaccuracies and types of error in the e-prescriptions were recorded and analysis was conducted to understand the effect of prescription complexity on this. The two prescriptions were discussed with participants to determine the causes of inaccuracies. Inaccuracies were found in 43% (13/30) of e-prescriptions and were significantly associated with alternating medication regimens (p = 0.025). There were four error types found within the e-prescriptions: inaccurate medication list, incorrect medication duration, incorrect medication frequency and prescription duplication errors. Medication list errors were significantly associated with alternating medication regimens (p = 0.007). Causes of e-prescription inaccuracy were due to failure to update the prescription following a change, errors in prescription entry and inaccuracies caused by using two different nebuliser devices. CFHealthHub e-prescriptions contain inaccuracies and prescription complexity can increase the risk of prescription inaccuracy, although the small sample size limits the ability of the service evaluation to draw strong conclusions. Causes of e-prescription accuracy should be addressed by the local CF team.

A mixed-methods needs assessment to identify pharmacology education objectives for emergency medicine residents.

ObjectivesMedication errors represent a significant threat to patient safety. Pharmacotherapy is one of the 23 Accreditation Council of Graduate Medical Education milestones for emergency medicine, yet there is minimal understanding of what content should be prioritized during training. The study aim was to develop objectives for a patient‐safety focused pharmacology curriculum for emergency medicine residents.MethodsWe incorporated data from a de‐identified safety event database and survey responses of 30 faculty and clinical pharmacists at a single‐site suburban university hospital with 24‐hour emergency medicine pharmacists and an annual volume of approximately 70,000. We reviewed the database to quantify types and severity of medication errors over a 5‐year period for a total of 370 errors. Anonymous surveys included categorical items that we analyzed with descriptive statistics and short answer questions that underwent thematic analysis by 2 coders. We summarized all data sources to identify curriculum gaps.ResultsCommon medication errors reported in our database were wrong dose (43%) and computer order entry errors (14%). Knowledge gaps were medication cost (63%), pregnancy risk information (60%), antibiotic stewardship (53%), interactions (47%), and side effects (47%). Qualitative analysis revealed the need to optimize computer order entry, understand the scope of critical medications, use references, and consult pharmacists. Integration of data suggested specific medications should be covered in curricular efforts, including antibiotics, analgesics, sedatives, and insulin.ConclusionWe developed objectives of pharmacology topics to prioritize during emergency medicine training to enhance prescribing safety. This study is limited due to its small sample and single institution source of data. Future studies should investigate the impact of pharmacology curriculum on minimizing clinical errors.

Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text.

The wide-scale adoption of electronic health records (EHRs) provides extensive information to support precision medicine and personalized health care. In addition to structured EHRs, we leverage free-text clinical information extraction (IE) techniques to estimate optimal dynamic treatment regimes (DTRs), a sequence of decision rules that dictate how to individualize treatments to patients based on treatment and covariate history. The proposed IE of patient characteristics closely resembles "The clinical Text Analysis and Knowledge Extraction System" and employs named entity recognition, boundary detection, and negation annotation. It also utilizes regular expressions to extract numerical information. Combining the proposed IE with optimal DTR estimation, we extract derived patient characteristics and use tree-based reinforcement learning (T-RL) to estimate multistage optimal DTRs. IE significantly improved the estimation in counterfactual outcome models compared to using structured EHR data alone, which often include incomplete data, data entry errors, and other potentially unobserved risk factors. Moreover, including IE in optimal DTR estimation provides larger study cohorts and a broader pool of candidate tailoring variables. We demonstrate the performance of our proposed method via simulations and an application using clinical records to guide blood pressure control treatments among critically ill patients with severe acute hypertension. This joint estimation approach improves the accuracy of identifying the optimal treatment sequence by 14-24% compared to traditional inference without using IE, based on our simulations over various scenarios. In the blood pressure control application, we successfully extracted significant blood pressure predictors that are unobserved or partially missing from structuredEHR.