scholarly journals Screening for medication errors using an outlier detection system

2017 ◽  
Vol 24 (2) ◽  
pp. 281-287 ◽  
Author(s):  
Gordon D Schiff ◽  
Lynn A Volk ◽  
Mayya Volodarskaya ◽  
Deborah H Williams ◽  
Lake Walsh ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Medication Errors ◽  
Detection System ◽  
Clinical Decision ◽  
Screening System ◽  
Coding System ◽  
Clinical Value ◽  
Record System ◽  
Study Objective ◽  
Alerting System

Objective: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. Materials and Methods: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012–2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts. Results: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues. Discussion: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data. Conclusion: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients’ clinical contexts and details.

scholarly journals Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4222
Author(s):  
Shushi Namba ◽  
Wataru Sato ◽  
Masaki Osumi ◽  
Koh Shimokawa
Keyword(s):  
Affective Computing ◽  
Detection System ◽  
Characteristic Curve ◽  
Unmet Need ◽  
Systematic Evaluation ◽  
Dynamic Mode ◽  
Coding System ◽  
Static Mode ◽  
Action Unit ◽  
Facial Action

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.

scholarly journals Developing Clinical Decision Support within a Commercial Electronic Health Record System to Improve Antimicrobial Prescribing in the Neonatal ICU

2014 ◽  
Vol 05 (02) ◽  
pp. 368-387 ◽  
Author(s):  
K. Cato ◽  
B. Sheehan ◽  
S. Patel ◽  
J. Duchon ◽  
P. DeLaMora ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Antimicrobial Prescribing ◽  
Neonatal Icu ◽  
Electronic Health

SummaryObjective: To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool.Methods: Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool.Results: The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%).Discussion: During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved.Conclusion: To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.Citation: Hum RS, Cato K, Sheehan B, Patel S, Duchon J, DeLaMora P, Ferng YH, Graham P, Vawdrey DK, Perlman J, Larson E, Saiman L. Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU. Appl Clin Inf 2014; 5: 368–387 http://dx.doi.org/10.4338/ACI-2013-09-RA-0069

scholarly journals Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

2016 ◽  
Vol 8s2 ◽  
pp. BII.S40208
Author(s):  
Sripriya Rajamani ◽  
Aaron Bieringer ◽  
Stephanie Wallerius ◽  
Daniel Jensen ◽  
Tamara Winden ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Population Based ◽  
Direct Access ◽  
Care Providers ◽  
Study Objective ◽  
Vaccination Rates ◽  
Electronic Health

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

scholarly journals Role of the Novel Integrated Clinical Pharmacist Menu Software in Improving Medication Therapy Management

2021 ◽  
pp. 103-114
Author(s):  
Muhammad Tahir Aziz ◽  
Toofeeq Ur Rehman ◽  
Sadia Qureshi ◽  
Kashif Sajjad
Keyword(s):  
Decision Support ◽  
Medication Errors ◽  
Clinical Decision Support ◽  
Clinical Pharmacist ◽  
Clinical Decision ◽  
Medication Therapy Management ◽  
Decision Support Tools ◽  
Medication Therapy ◽  
Therapeutic Outcomes ◽  
Support Tools

Background: Medication therapy management (MTM) continues to offer pharmacists the opportunity to use their knowledge, assist patients and caregiver in improving therapeutic outcomes, however the change is slow. Health information technology has been noted as an important driver in the success of MTM and has a potential role in improving therapeutic outcomes and reducing medication errors. Objective: This research aimed to design an integrated clinical pharmacist menu (CPM) software along with clinical decision support tools, optimizing MTM services and reducing medication errors. Methods: The integrated CPM software was designed abridged with decision support tools. A comparative study was conducted in a setting of integrated CPM software versus paper-based clinical pharmacy services (P-CPS) for the evaluation of MTM services. Clinical decision support systems (CDSS) and automated significant laboratory and medication alerts were analyzed for the improvement of MTM and impact on the identification and resolution of medication errors. Results: MTM improved after the application of the CPM software with a difference of 100% in “medication history generation” and “patient care plan,” with a reduction in medication errors by 39.8%. The identification of medication errors and verification of medication order significantly improved from 49% to 82% (p = 0.00) and from 4.5% to 7.0% (p = 0.00), respectively, in the CPM setting. The CDSS tool in the CPM software generated 730, 1802, and 198 auto alerts for “drug–drug interaction,” “inappropriate dose,” and “dose adjustment in an abnormal clinical laboratory test,” respectively, which improved the resolution and identification of medication errors. Conclusion: The CPM is user-friendly, which improved the MTM services. Medication error identification and resolution were significantly improved by the CPM software.

Combined data mining techniques based patient data outlier detection for healthcare safety

2016 ◽  
Vol 9 (1) ◽  
pp. 42-68 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Patient Safety ◽  
Outlier Detection ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Content Type ◽  
Outliers Detection

Purpose – Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular. Design/methodology/approach – It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications. Findings – The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works. Research limitations/implications – This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems. Originality/value – DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

Two-center comparison of 10 fully-automated commercial procalcitonin (PCT) immunoassays

2019 ◽  
Vol 58 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Giuseppe Lippi ◽  
Gian Luca Salvagno ◽  
Matteo Gelati ◽  
Mairi Pucci ◽  
Claudia Lo Cascio ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Multiple Comparisons ◽  
Clinical Decision ◽  
Plasma Samples ◽  
Clinical Value ◽  
Heparin Plasma ◽  
Variable Bias ◽  
Abbott Architect ◽  
Roche Cobas ◽  
Center Study

Abstract Background This two-center study was designed to verify comparability of procalcitonin (PCT) values among 10 different commercial immunoassays. Methods A total number of 176 routine lithium-heparin plasma samples were divided in identical aliquots and simultaneously analyzed with 10 different PCT immunoassays, including Kryptor BRAHMS PCT sensitive, Abbott Architect BRAHMS PCT, Beckman Coulter Access PCT (on Access and DXI), BioMérieux Vidas BRAHMS PCT, Diasorin Liaison BRAHMS PCT, Fujirebio Lumipulse G BRAHMS PCT, Roche BRAHMS PCT (on Cobas E801), Diazyme PCT (on Roche Cobas C702) and SNIBE Maglumi PCT. Results Highly significant correlation was always found across multiple comparisons, with correlation coefficients comprised between 0.918 and 0.997 (all p < 0.001). Bland and Altman plots analysis revealed highly variable bias among immunoassays, ranging between ±0.2% and ±38.6%. Diazyme PCT on Roche Cobas C702 and SNIBE Maglumi PCT displayed the larger overestimation, whilst PCT values were underestimated by Cobas BRAHAMS PCT. The agreement was always >80% (all p < 0.001), but varied largely across multiple comparisons, ranging between 90%–99% at 0.1 μg/L, 81%–99% at 0.25 μg/L, 83%–100% at 0.5 μg/L, 94%–100% at 2.0 μg/L and 90%–99% at 10 μg/L, respectively. The larger disagreement was observed comparing Diazyme PCT and Maglumi PCT with the other methods. Conclusions Although we found acceptable correlation among 10 commercial PCT immunoassays, the limited agreement at clinical decision thresholds remains a major issue, especially at lower end of PCT concentration, thus potentially contributing to jeopardize the clinical value of this biomarker.

scholarly journals Clinical value of measurable residual disease testing for assessing depth, duration, and direction of response in multiple myeloma

2020 ◽  
Vol 4 (14) ◽  
pp. 3295-3301
Author(s):  
Joaquin Martinez-Lopez ◽  
Sandy W. Wong ◽  
Nina Shah ◽  
Natasha Bahri ◽  
Kaili Zhou ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Decision Making ◽  
Residual Disease ◽  
Clinical Care ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Routine Practice ◽  
Clinical Value ◽  
The Impact ◽  
Measurable Residual Disease

Abstract Few clinical studies have reported results of measurable residual disease (MRD) assessments performed as part of routine practice. Herein we present our single-institution experience assessing MRD in 234 multiple myeloma (MM) patients (newly diagnosed [NDMM = 159] and relapsed [RRMM = 75]). We describe the impact of depth, duration, and direction of response on prognosis. MRD assessments were performed by next-generation sequencing of immunoglobulin genes with a sensitivity of 10−6. Those achieving MRD negativity at 10−6, as well as 10−5, had superior median progression-free survival (PFS). In the NDMM cohort, 40% of the patients achieved MRD negativity at 10−6 and 59% at 10−5. Median PFS in the NDMM cohort was superior in those achieving MRD at 10−5 vs &lt;10−5 (PFS: 87 months vs 32 months; P &lt; .001). In the RRMM cohort, 36% achieved MRD negativity at 10−6 and 47% at 10−5. Median PFS was superior for the RRMM achieving MRD at 10−5 vs &lt;10−5 (PFS: 42 months vs 17 months; P &lt; .01). Serial MRD monitoring identified 3 categories of NDMM patients: (A) patients with ≥3 MRD 10−6 negative samples, (B) patients with detectable but continuously declining clonal numbers, and (C) patients with stable or increasing clonal number (≥1 log). PFS was superior in groups A and B vs C (median PFS not reached [NR], NR, 55 respectively; P &lt; .001). This retrospective evaluation of MRD used as part of clinical care validates MRD as an important prognostic marker in NDMM and RRMM and supports its use as an endpoint in future clinical trials as well as for clinical decision making.

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