scholarly journals Using Markov Models to Characterize and Predict Process Target Compliance

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1187
Author(s):  
Sally McClean
Keyword(s):  
Performance Indicators ◽  
Markov Models ◽  
Process Mining ◽  
Industrial Processes ◽  
Satisfactory Performance ◽  
Number Of Patients ◽  
Future Events ◽  
Using Data ◽  
Exit Options ◽  
Completion Times

Processes are everywhere, covering disparate fields such as business, industry, telecommunications, and healthcare. They have previously been analyzed and modelled with the aim of improving understanding and efficiency as well as predicting future events and outcomes. In recent years, process mining has appeared with the aim of uncovering, observing, and improving processes, often based on data obtained from logs. This typically requires task identification, predicting future pathways, or identifying anomalies. We here concentrate on using Markov processes to assess compliance with completion targets or, inversely, we can determine appropriate targets for satisfactory performance. Previous work is extended to processes where there are a number of possible exit options, with potentially different target completion times. In particular, we look at distributions of the number of patients failing to meet targets, through time. The formulae are illustrated using data from a stroke patient unit, where there are multiple discharge destinations for patients, namely death, private nursing home, or the patient’s own home, where different discharge destinations may require disparate targets. Key performance indicators (KPIs) of this sort are commonplace in healthcare, business, and industrial processes. Markov models, or their extensions, have an important role to play in this work where the approach can be extended to include more expressive assumptions, with the aim of assessing compliance in complex scenarios.

Assessing Efficiency in General Practice: An Application of Data Envelopment Analysis

1998 ◽  
Vol 11 (2) ◽  
pp. 103-108 ◽  
Author(s):  
J. M. Bates ◽  
D. L. Baines ◽  
D. K. Whynes
Keyword(s):  
Health Care ◽  
General Practice ◽  
Data Envelopment Analysis ◽  
Performance Indicators ◽  
Care Process ◽  
Considerable Importance ◽  
Data Envelopment ◽  
Number Of Patients ◽  
Efficiency Ranking ◽  
Using Data

As with any health care process, the efficiency with which outputs are produced in general practice is of considerable importance. Using data from Lincolnshire, this study utilizes data envelopment analysis to examine the relationships between practice costs and outputs, measured not only as the number of patients treated, but also on the basis of performance indicators. The technique permits the construction of an efficiency ranking, facilitating the accurate targeting of monitoring resources.

scholarly journals Comorbidity index for predicting mortality at 6 months after reperfusion therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyo Suk Nam ◽  
Young Dae Kim ◽  
Joonsang Yoo ◽  
Hyungjong Park ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Prospective Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Reperfusion Therapy ◽  
Good Prediction ◽  
Multivariate Logistic Regression ◽  
Stroke Patients ◽  
Number Of Patients ◽  
Comorbidity Index ◽  
Using Data

AbstractThe eligibility of reperfusion therapy has been expanded to increase the number of patients. However, it remains unclear the reperfusion therapy will be beneficial in stroke patients with various comorbidities. We developed a reperfusion comorbidity index for predicting 6-month mortality in patients with acute stroke receiving reperfusion therapy. The 19 comorbidities included in the Charlson comorbidity index were adopted and modified. We developed a statistical model and it was validated using data from a prospective cohort. Among 1026 patients in the retrospective nationwide reperfusion therapy registry, 845 (82.3%) had at least one comorbidity. As the number of comorbidities increased, the likelihood of mortality within 6 months also increased (p < 0.001). Six out of the 19 comorbidities were included for developing the reperfusion comorbidity index on the basis of the odds ratios in the multivariate logistic regression analysis. This index showed good prediction of 6-month mortality in the retrospective cohort (area under the curve [AUC], 0.747; 95% CI, 0.704–0.790) and in 333 patients in the prospective cohort (AUC, 0.784; 95% CI, 0.709–0.859). Consideration of comorbidities might be helpful for the prediction of the 6-month mortality in patients with acute ischemic stroke who receive reperfusion therapy.

scholarly journals Reported Diabetes Mellitus Prevalence Rates in the Colombia Healthcare System from 2009 to 2012: Analysis by Regions Using Data of the Official Information Sources

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Noël C. Barengo ◽  
Diana Carolina Tamayo
Keyword(s):  
Diabetes Mellitus ◽  
Healthcare System ◽  
Social Protection ◽  
Prevalence Rates ◽  
Annual Increase ◽  
Number Of Patients ◽  
High Expenditure ◽  
System Information ◽  
Using Data ◽  
Official Information

The objective of this study was to describe the reported diabetes mellitus (DM) prevalence rates of the 20–79-year-old population in Colombia from 2009 to 2012 reported by the healthcare system. Information on number of patients treated for DM was obtained by the Integral Information System of Social Protection (SISPRO), the registry of the Ministry of Health and Social Protection, and the High Cost Account (CAC), an organization to trace high expenditure diseases. From both sources age-standardized reported DM prevalence rates per 100.000 inhabitants from 2009 to 2012 were calculated. Whereas the reported DM prevalence rates of SISPRO revealed an increase from 964/100.000 inhabitants (2009) to 1398/100.000 inhabitants in 2012 (mean annual increase 141/100.000;pvalue: 0.001), the respective rates in the CAC register were 1082/100.000 (2009) and 1593/100.000 in 2012 (mean annual increase 165/100.000;pvalue: 0.026). The number of provinces reporting not less than 19% of the highest national reported DM prevalence rates (1593/100.000) increased from two in 2009 to ten in 2012. Apparently, the registries and the information retrieving system have been improved during 2009 and 2012, resulting in a greater capacity to identify and report DM cases by the healthcare system.

scholarly journals Hemodialysis services: are public policies turned to guaranteeing the access?

2015 ◽  
Vol 31 (7) ◽  
pp. 1505-1516 ◽  
Author(s):  
Ana Rita Barbieri ◽  
Crhistinne Cavalheiro Maymone Gonçalves ◽  
Maria de Fátima Meinberg Cheade ◽  
Cristina Souza ◽  
Daniel Henrique Tsuha ◽  
...  
Keyword(s):  
Medical Records ◽  
Health Informatics ◽  
Ecological Study ◽  
Public Policies ◽  
National Average ◽  
Mato Grosso ◽  
Number Of Patients ◽  
Using Data ◽  
Mato Grosso Do Sul

The increasing incidence of chronic renal failure in Brazil and the consequential expansion of hemodialysis as a choice for treatment in final stage have to be taken into account to guarantee access to those in need. The ecological study conducted in Mato Grosso do Sul State, Brazil, in 2012, using data from the Brazilian Health Informatics Department (DATASUS) and from the analysis of medical records in 12 clinics, identified and mapped patients on hemodialysis, the distance they travelled and the estimated number of patients. The prevalence of hemodialysis patients in Mato Grosso do Sul State, about 55 per 100,000 inhabitants, is similar to the national average. The analyses indicated concentration of patients in counties with clinics and also geographical gaps that generate displacement of over 100km for more than 16% of patients. The results point to the necessity of strengthening public policies that consider, for decision-making, the decentralization of service, the expansion of home care and the follow-up education for professionals.

scholarly journals Business Intelligence Development in Distributed Information Systems to Visualized Predicting and Give Recommendation for Handling Dengue Hemorrhagic Fever

2020 ◽  
Vol 6 (1) ◽  
pp. 55
Author(s):  
Radityo Prasetianto Wibowo ◽  
Wiwik Anggraeni ◽  
Tresnaning Arifiyah ◽  
Edwin Riksakomara ◽  
Febriliyan Samopa ◽  
...  
Keyword(s):  
Dengue Fever ◽  
Dengue Hemorrhagic Fever ◽  
Business Intelligence ◽  
Success Factors ◽  
Hemorrhagic Fever ◽  
Distributed Information ◽  
Development Method ◽  
Fever Patient ◽  
Number Of Patients ◽  
Using Data

 Background: Indonesia has 150 dengue cases every month, and more than one person dies every day from 2017 to 2020. One of the factors of Dengue Hemorrhagic Fever (DHF) patients dying is due to the late handling of patients in hospitals or clinics. Health Office of Malang Regency recorded 1,114 cases of DHF that occurred during 2016, and the number of patients room available is limited. Therefore, Malang Regency is used as a case study in this research.Objective: This study aims to make a dashboard to display the predictions, visualize the distribution of DHF patients, and give mitigation recommendations for handling DHF patients in Malang Health Office.Methods: This study used the Business Intelligence (BI) Development method, which consists of two main phases, namely the making of Business Intelligence and the use of Business Intelligence. This research used the making of the BI phase, which consists of four stages, which are BI development strategies, identification and preparation of data sources, selecting BI tools, and designing and implementing BI. In the Extract, Load, and Transform process, this study used essential transformation and forecast.Results: BI method has succeeded in building the dashboard. The dashboard displays the visualization of Dengue Hemorrhagic Fever predicted results, detail of Dengue Fever Patient number, Dengue Fever patient trends per year and predictions 2 Monthly patient, and mitigation recommendation for each Community Health Office.Conclusion: We have built the BI Dashboard using the BI development method. It needs some treatment to get better performance. These are improving ETL performance using data virtualization technology, considering the use of cloud computing technology, conducting further evaluations by understanding the critical success factors to determine the level of success and weaknesses.

scholarly journals Cervical arterial dissection: clinical characteristics in a neurology service in São Paulo, Brazil

2019 ◽  
Vol 77 (9) ◽  
pp. 632-637
Author(s):  
Danyelle Sadala Reges ◽  
Marcela Mazzeo ◽  
Rafael Rosalino ◽  
Vivian Dias Baptista Gagliardi ◽  
Leandro Gama Cerqueira ◽  
...  
Keyword(s):  
Medical Records ◽  
Clinical Characteristics ◽  
Rare Event ◽  
Clinical Results ◽  
Medical Professional ◽  
Arterial Dissection ◽  
Common Risk Factor ◽  
Patients Âgés ◽  
Number Of Patients ◽  
Using Data

ABSTRACT Cervical arterial dissection accounts for only a small proportion of ischemic stroke but arouses scientific interest due to its wide clinical variability. Objective: This study aimed to evaluate its risk factors, outline its clinical characteristics, compare treatment with antiaggregation or anticoagulation, and explore the prognosis of patients with cervical arterial dissection. Methods: An observational, retrospective study using data from medical records on patients with cervical arterial dissection between January 2010 and August 2015. Results: The total number of patients was 41. The patients' ages ranged from 19 to 75 years, with an average of 44.5 years. The most common risk factor was smoking. Antiaggregation was used in the majority of patients (65.5%); 43% of all patients recanalized in six months, more frequently in patients who had received anticoagulation (p = 0.04). Conclusion: The presence of atherosclerotic disease is considered rare in patients with cervical arterial dissection; however, our study found a high frequency of hypertension, smoking and dyslipidemia. The choice of antithrombotic remains controversial and will depend on the judgment of the medical professional; the clinical results with anticoagulation or antiaggregation were similar but there was more recanalization in the group treated with anticoagulation; its course was favorable in both situations. The recurrence of cervical arterial dissection and stroke is considered a rare event and its course is favorable.

Model Data

2021 ◽  
pp. 95-188
Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker
Keyword(s):  
Real World ◽  
Diabetes Patient ◽  
Model Data ◽  
Real World Data ◽  
Future Events ◽  
Patient’S Information ◽  
The Subject ◽  
Modeling Data ◽  
Using Data ◽  
World Environment

After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.

scholarly journals P138: Using electronic health record data to assess emergency medicine trainees independent and interdependent performance: a qualitative perspective on measuring what matters

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S106-S106
Author(s):  
L. Shepherd ◽  
S. Sebok-Syer ◽  
L. Lingard ◽  
A. McConnell ◽  
R. Sedran ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Electronic Health Record ◽  
Performance Indicators ◽  
Clinical Performance ◽  
Turnaround Time ◽  
Health Record ◽  
Structured Interviews ◽  
Electronic Health Record Data ◽  
Number Of Patients ◽  
Electronic Health

Introduction: Competency-based medical education (CBME) affirms that trainees will receive timely assessments and effective feedback about their clinical performance, which has inevitably raised concerns about assessment burden. Therefore, we need ways of generating assessments that do not rely exclusively on faculty-produced reports. The main object of this research is to investigate how data already collected in the electronic health record (EHR) might be meaningfully and appropriately used for assessing emergency medicine (EM) trainees independent and interdependent clinical performance. This study represents the first step in exploring what EHR data might be utilized to monitor and assess trainees clinical performance Methods: Following constructivist grounded theory, individual semi-structured interviews were conducted with 10 EM faculty and 11 EM trainees, across all postgraduate years, to identify EHR performance indicators that represent EM trainees independent and interdependent clinical actions and decisions. Participants were presented with a list of performance indicators and asked to comment on how valuable each would be in assessing trainee performance. Data analysis employed constant comparative inductive methods and occured throughout data collection. Results: Participants created, refined, and eliminated performance indicators. Our main result is a catalogue of clinical performance indicators, described by our participants, as reflecting independent and/or interdependent EM trainee performance that are believed to be captured within the EHR. Such independent indicators include: number of patients seen (according to CTAS levels), turnaround time between when a patient is signed up for and first orders are made, number of narcotics prescribed. Meanwhile, interdependent indicators include, but are not limited to, length of stay, bounce-back rates, ordering practices, and time to fluids. Conclusion: Our findings document a process for developing EM trainee report cards that incorporate the perspectives of clinical faculty and trainees. Our work has important implications for distinguishing between independent and interdependent clinical performance indicators.

scholarly journals Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

2020 ◽  
Vol 17 (19) ◽  
pp. 7080 ◽  
Author(s):  
Abdelrahman E. E. Eltoukhy ◽  
Ibrahim Abdelfadeel Shaban ◽  
Felix T. S. Chan ◽  
Mohammad A. M. Abdel-Aal
Keyword(s):  
Data Analytics ◽  
Historical Data ◽  
External Factors ◽  
Number Of Patients ◽  
Future Prediction ◽  
The Government ◽  
Using Data ◽  
Novel Coronavirus ◽  
Improved Accuracy ◽  
Nonlinear Autoregressive

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

scholarly journals Perspectives of University-Industry Technology Transfer in African Emerging Economies: Evaluating the Nigerian Scenario via a Data Envelopment Approach

2019 ◽  
Vol 8 (10) ◽  
pp. 286 ◽  
Author(s):  
Oluwaseun Fadeyi ◽  
Petra Maresova ◽  
Ruzena Stemberkova ◽  
Micheal Afolayan ◽  
Funminiyi Adeoye
Keyword(s):  
Technology Transfer ◽  
Emerging Economies ◽  
Performance Indicators ◽  
Data Envelopment ◽  
Technology Transfer Office ◽  
Innovation Transfer ◽  
Using Data ◽  
University Industry ◽  
Infant Stage ◽  
Resource Input

All of Africa’s emerging economies are faced with developmental challenges, which can be partly ameliorated using effective University–Industry technology transfer. While technology transfer remains at the infant stage, sparsely documented, and with no complex ongoing processes in many African societies, Universities in Africa are making efforts in University–Industry collaborations aimed at bringing significant improvements to the continent in a bid to drive national innovation and regional economic development. In this paper, we attempt to evaluate the progress made so far by Nigerian Universities in technological innovation transfer, in order to suggest ways for possible future progress. To do this, crucial technology transfer resource factors (inputs), namely, the number of linkage projects funded by the “African Research Council” (ARC), consortium membership of the University’s technology transfer office, and the number of doctoral staff at the University’s technology transfer office, were checked against a set of performance measures (number of executed licenses, amount of licensing royalty income, number of spin-offs created, and the number of spin-offs created with university equity), using data envelopment analysis and multiple regression, respectively. Results suggest that Universities that possess better resource factors reported higher outputs on most of the performance indicators applied. In addition, it was observed that Universities with greater ability to effectively transfer knowledge had higher technology commercialization performance and financial sustainability. The implication of these results is that Universities in Africa need to develop in line with the technology transfer resource (input) factors suggested within this study, as this is the way to go for better performance.

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