scholarly journals A Classification System for Decision-Making in the Management of Patients with Chronic Conditions

2021 ◽  
Vol 13 (23) ◽  
pp. 13176
Author(s):  
Francisco Ródenas-Rigla ◽  
David Conesa ◽  
Antonio López-Quílez ◽  
Estrella Durá-Ferrandis
Keyword(s):  
Decision Making ◽  
Hospital Admission ◽  
Chronic Conditions ◽  
Linear Models ◽  
Statistical Processing ◽  
Target Population ◽  
Healthcare Services ◽  
Classification Systems ◽  
Classifier Systems ◽  
Health Care Centre

Patients with chronic diseases are frequent users of healthcare services. The systematic use of stratification tools and predictive models for this group of patients can be useful for health professionals in decision-making processes. The aim of this study was to design two new classifier systems for detecting the risk of hospital admission for elderly patients with chronic conditions. In this retrospective cohort study, a set of variables related to hospital admission for patients with chronic conditions was obtained through focus groups, a health database analysis and statistical processing. To predict the probability of admission from the set of predictor variables, a logistic regression within the framework of Generalized Linear Models was used. The target population consisted of patients aged 65 years or older treated in February 2016 at the Primary Health Care Centre of Burjassot (Spain). This sample was selected through the consecutive sampling of the patient quotas of the physicians who participated in the study (1000 patients). The result was two classification systems, with reasonable values of 0.722 and 0.744 for the area under the ROC curve. The proposed classifier systems could facilitate a change in the current patient management models and make them more proactive.

Evaluations for New Healthcare Environment Commissioning and Operational Decision Making Using Simulation and Human Factors: A Case Study of an Interventional Trauma Operating Room

2021 ◽  
pp. 193758672199966
Author(s):  
Mirette Dubé ◽  
Jason Laberge ◽  
Elaine Sigalet ◽  
Jonas Shultz ◽  
Christine Vis ◽  
...  
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Operating Room ◽  
Healthcare Services ◽  
Healthcare Team ◽  
Healthcare Environment ◽  
New Process ◽  
Timely Access ◽  
Operational Decision Making

Purpose: The aim of this article is to provide a case study example of the preopening phase of an interventional trauma operating room (ITOR) using systems-focused simulation and human factor evaluations for healthcare environment commissioning. Background: Systems-focused simulation, underpinned by human factors science, is increasingly being used as a quality improvement tool to test and evaluate healthcare spaces with the stakeholders that use them. Purposeful real-to-life simulated events are rehearsed to allow healthcare teams opportunity to identify what is working well and what needs improvement within the work system such as tasks, environments, and processes that support the delivery of healthcare services. This project highlights salient evaluation objectives and methods used within the clinical commissioning phase of one of the first ITORs in Canada. Methods: A multistaged evaluation project to support clinical commissioning was facilitated engaging 24 stakeholder groups. Key evaluation objectives highlighted include the evaluation of two transport routes, switching of operating room (OR) tabletops, the use of the C-arm, and timely access to lead in the OR. Multiple evaluation methods were used including observation, debriefing, time-based metrics, distance wheel metrics, equipment adjustment counts, and other transport route considerations. Results: The evaluation resulted in several types of data that allowed for informed decision making for the most effective, efficient, and safest transport route for an exsanguinating trauma patient and healthcare team; improved efficiencies in use of the C-arm, significantly reduced the time to access lead; and uncovered a new process for switching OR tabletop due to safety threats identified.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Innovative Strategies to Facilitate Patient-Centered Research in Multiple Chronic Conditions

2021 ◽  
Vol 10 (10) ◽  
pp. 2112
Author(s):  
Tullika Garg ◽  
Courtney A. Polenick ◽  
Nancy Schoenborn ◽  
Jane Jih ◽  
Alexandra Hajduk ◽  
...  
Keyword(s):  
Chronic Conditions ◽  
Clinical Care ◽  
Intervention Development ◽  
Healthcare Services ◽  
Multiple Chronic Conditions ◽  
Patient Centered ◽  
Older Americans ◽  
Innovative Strategies ◽  
Department Of Health ◽  
Innovative Methods

Multiple chronic conditions (MCC) are one of today’s most pressing healthcare concerns, affecting 25% of all Americans and 75% of older Americans. Clinical care for individuals with MCC is often complex, condition-centric, and poorly coordinated across multiple specialties and healthcare services. There is an urgent need for innovative patient-centered research and intervention development to address the unique needs of the growing population of individuals with MCC. In this commentary, we describe innovative methods and strategies to conduct patient-centered MCC research guided by the goals and objectives in the Department of Health and Human Services MCC Strategic Framework. We describe methods to (1) increase the external validity of trials for individuals with MCC; (2) study MCC epidemiology; (3) engage clinicians, communities, and patients into MCC research; and (4) address health equity to eliminate disparities.

scholarly journals Factors influencing decision making for implementing e-health in light of the COVID-19 outbreak in Gulf Cooperation Council countries

2021 ◽  
Author(s):  
Fahad M Al-Anezi
Keyword(s):  
Decision Making ◽  
United Arab Emirates ◽  
Healthcare Access ◽  
Demographic Factors ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Gulf Cooperation Council ◽  
Factors Influencing ◽  
Electronic Health ◽  
Novel Coronavirus

Abstract Background Electronic health (e-health) approaches such as telemedicine, mobile health, virtual healthcare and electronic health records are considered to be effective in increasing access to healthcare services, reducing operational costs and improving the quality of healthcare services during the coronavirus disease 2019 (COVID-19) outbreak, a pandemic resulting from the spread of a novel coronavirus discovered in December 2019. In this context, the aim of this study was to identify the most important factors influencing decision making on the implementation of e-health in Gulf Cooperation Council (GCC) member states (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates), which are in the process of digitizing healthcare services. Methods This study reviewed the literature to identify the important factors influencing decision making on e-health. In addition, a questionnaire-based survey was conducted in order to identify the most important criteria to be considered in decision making on e-health. The survey link was forwarded to 978 healthcare practitioners and 42 experts (purposive sampling), from which a final sample of 892 (864 practitioners and 28 experts) was achieved, reflecting a response rate of 87.45%. Results Of the 44 factors identified under seven themes (strategic, quality, management, technology, function characteristics, economic, sociocultural and demographic factors), 22 factors were identified to be the most important criteria. Conclusions Findings from this study suggest that decision making in relation to e-health is a complex process that requires consideration of various factors. It was also found that attention should be paid to sociocultural and demographic factors, which may need to be considered in increasing healthcare access during the COVID-19 outbreak.

scholarly journals Toward the Development of a Comprehensive Clinically Oriented Patient Profile: A Systematic Review of the Purpose, Characteristic, and Methodological Quality of Classification Systems of Adult Spinal Deformity

Neurosurgery ◽  
2021 ◽  
Author(s):  
Kenny Yat Hong Kwan ◽  
J Naresh-Babu ◽  
Wilco Jacobs ◽  
Marinus de Kleuver ◽  
David W Polly ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Classification System ◽  
Methodological Quality ◽  
Literature Search ◽  
Adult Spinal Deformity ◽  
Classification Systems ◽  
Systematic Literature Search ◽  
Patient Profile

Abstract BACKGROUND Existing adult spinal deformity (ASD) classification systems are based on radiological parameters but management of ASD patients requires a holistic approach. A comprehensive clinically oriented patient profile and classification of ASD that can guide decision-making and correlate with patient outcomes is lacking. OBJECTIVE To perform a systematic review to determine the purpose, characteristic, and methodological quality of classification systems currently used in ASD. METHODS A systematic literature search was conducted in MEDLINE, EMBASE, CINAHL, and Web of Science for literature published between January 2000 and October 2018. From the included studies, list of classification systems, their methodological measurement properties, and correlation with treatment outcomes were analyzed. RESULTS Out of 4470 screened references, 163 were included, and 54 different classification systems for ASD were identified. The most commonly used was the Scoliosis Research Society-Schwab classification system. A total of 35 classifications were based on radiological parameters, and no correlation was found between any classification system levels with patient-related outcomes. Limited evidence of limited quality was available on methodological quality of the classification systems. For studies that reported the data, intraobserver and interobserver reliability were good (kappa = 0.8). CONCLUSION This systematic literature search revealed that current classification systems in clinical use neither include a comprehensive set of dimensions relevant to decision-making nor did they correlate with outcomes. A classification system comprising a core set of patient-related, radiological, and etiological characteristics relevant to the management of ASD is needed.

Shared Decision Making in Common Chronic Conditions: Impact of a Resident Training Workshop

2016 ◽  
Vol 28 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Leigh Simmons ◽  
Lauren Leavitt ◽  
Alaka Ray ◽  
Blair Fosburgh ◽  
Karen Sepucha
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Chronic Conditions ◽  
Resident Training ◽  
Shared Decision ◽  
Training Workshop

From epidemiology to public health decision making: analysis on IMD & vaccination strategy in Italy

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Igidbashian ◽  
F Caracci ◽  
P Bonanni ◽  
P Castiglia ◽  
M Conversano ◽  
...  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Adult Population ◽  
Vaccination Strategy ◽  
Healthcare Services ◽  
Steady Increase ◽  
Vaccine Preventable Disease ◽  
Preventable Disease ◽  
Italian Regions ◽  
Age Range

Abstract Introduction Invasive Meningococcal Disease (IMD) is one of the most severe vaccine-preventable disease, with high fatality rate and severe sequelae in up to 20% of survivors. MenB, MenC and MenACWY vaccines are available in Italy, but recommendations vary among Italian regions in terms of type of vaccines and targeted age groups. The aim of the study is to describe epidemiology of IMDs in order to provide the best vaccination strategy. Methods IMDs surveillance data in the period 2011-2017 from the Italian National Health Institute were explored. Excel was used to present trend analysis, stratifying by age and serogroups. Results In Italy, during the period 2011-2017, IMDs overall incidence increased from 0.25 cases/100,000 inhabitants in 2011 to 0.33 in 2017. Most cases after 2013 were caused by non-B serogroups (52%, 52%, 66%, 64%, 59% from 2013 to 2017). Although incidence is highest in 1 years old children, the number of cases is highest in the age range 25-64. The number of cases in this age-range had a steady increase after 2013 (36 cases in 2011, 79 in 2017), with serogroups C, W and Y present in more than 65% of cases in 25+ age ranges after 2012. Conclusions IMD is a rare but severe vaccine-preventable disease. The key role of public health is to monitor disease serogroups, trends and outbreaks and strengthen methodological evidence-based tools for decision-making processes, public health policies, planning of healthcare services and intervention measures, including immunization. The increase in incidence shown in the period 2011-2017 in Italy, although probably due to better surveillance, highlighted the high circulation also of non-B serogroups and the importance of the disease in the adult population. Based on our analysis we believe that anti-meningococcal vaccination plan in Italy should include the highest number of preventable serogroups and be aimed to the whole population through a multicohort strategy, including boosters in children and in adults. Key messages Anti-meningococcal vaccination plan in Italy should include all the preventable serogroups and be aimed to the whole population with a multicohort strategy including boosters in children and in adults. The increase in incidence of IMD in the period 2011-2017 in Italy highlighted the high circulation also of non-B serogroups and the importance of the disease in the adult population.

Letter to the editor: Using classification systems to integrate ecosystem services with decision making tools

2021 ◽  
Vol 48 ◽  
pp. 101257
Author(s):  
John Finisdore ◽  
Karl A. Lamothe ◽  
Charles R. Rhodes ◽  
Carl Obst ◽  
Pieter Booth ◽  
...  
Keyword(s):  
Decision Making ◽  
Ecosystem Services ◽  
Classification Systems ◽  
Letter To The Editor

OP179 Quantitative Evidence Synthesis Methods For Assessing The Effectiveness Of Treatment Sequences For Clinical And Economic Decision-Making: Methodology Review

2021 ◽  
Vol 37 (S1) ◽  
pp. 4-4
Author(s):  
Ruth Lewis ◽  
Dyfrig Hughes ◽  
Alex Sutton ◽  
Clare Wilkinson
Keyword(s):  
Decision Making ◽  
Chronic Conditions ◽  
Evidence Synthesis ◽  
Policy Decision ◽  
Clinical Effectiveness ◽  
Synthesis Methods ◽  
Economic Decision Making ◽  
Treatment Sequencing ◽  
Policy Decision Making ◽  
Treatment Sequences

IntroductionThe sequential use of alternative treatments for chronic conditions represents a complex, dynamic intervention pathway; previous treatment and patient characteristics affect both choice and effectiveness of subsequent treatments. Evidence synthesis methods that produce the least biased estimates of treatment-sequencing effects are required to inform reliable clinical and policy decision-making. A comprehensive review was conducted to establish what existing methods are available, outline the assumptions they make, and identify their shortcomings.MethodsThe review encompassed both meta-analytic techniques and decision-analytic modelling, any disease condition, and any type of treatment sequence, but not diagnostic tests, screening, or treatment monitoring. It focused on the estimation of clinical effectiveness and did not consider the impact of treatment sequencing on the estimation of costs or utility values.ResultsThe review included ninety-one studies. Treatment-sequencing is usually dealt with at the decision-modelling stage and is rarely addressed using evidence synthesis methodology for clinical effectiveness. Most meta-analyses are of discrete treatments, sometimes stratified by line of therapy. Prospective sequencing trials are scarce. In their absence, there is no single best way to evaluate treatment sequences, rather there is a range of approaches, each of which has advantages and disadvantages and is influenced by the evidence available and the decision problem. Due to the scarcity of data on sequential treatments, modelling studies generally apply simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of decision-analytic models.ConclusionsThe evolution of network meta-analysis in HTA demonstrates that clinical and policy decision-making should account for the multiple treatments available for many chronic conditions. However, treatment-sequencing has yet to be accounted for within clinical evaluations. Economic modelling is often based on the simplifying assumption of treatment independence. This can lead to misrepresentation of the true level of uncertainty, potential bias in estimating the effectiveness and cost effectiveness of treatments and, eventually, the wrong decision.

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