1
40
3
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Text
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URL Address
<a href="http://doi.org/10.1111/j.1365-2753.2009.01264.x" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/j.1365-2753.2009.01264.x</a>
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Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
1118-1124
Issue
6
Volume
15
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Dublin Core
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Title
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Patient Computer Use To Prompt Doctor Adherence To Diabetes Management Guidelines
Publisher
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Journal of Evaluation in Clinical Practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2009
2009-12
Subject
The topic of the resource
complications; computer; delivery; diabetes; General & Internal; Health Care Sciences & Services; intervention; involvement; knowledge; management guidelines; Medical Informatics; Medicine; mellitus; patient; performance; preventive services; primary care; quality; risk; standards
Creator
An entity primarily responsible for making the resource
Haller N A; Gil K M; Gardner W G; Whittier F C
Description
An account of the resource
Rationale, Aims and Objectives Doctor compliance with diabetic care guidelines is low and may be improved with system-wide changes that include patient involvement. The objective of this study was to determine if patients in an internal medicine teaching clinic would use a touch-screen computer to receive personalized information regarding their need for diabetes care. Outcomes included determining if this intervention would improve resident doctor compliance with diabetic guidelines. Methods In this prospective study a computer was available for patients to use independently in one clinic, while another computer was placed in a second clinic with nursing support. Patients responding they were diabetic to the first screen received screens covering HbA1c, blood pressure, cholesterol, foot, eye examinations and compliance with having labs drawn. Non-diabetic patients received three general health screens. A response-based report was printed for patients to share with their doctor. Chart reviews were conducted to assess diabetic health care delivery. Results The computer was used voluntarily by 20.6% of patients in the primary clinic and by 100% of patients in the nurse-assisted clinic. A total of 104 patients from both clinics responded they were diabetic; over 50% did not know what HbA1c meant and a minority responded their HbA1c, blood pressure and cholesterol were at good levels. Significantly more HbA1c tests conducted within 6 months were documented in patients' charts. Discussion Patients used the computer effectively without direction in the primary clinic. In this initial study, implementation of the computer program increased the number of HbA1c tests ordered. Future studies incorporating refinements may increase both usage and efficacy of this intervention.
Identifier
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<a href="http://doi.org/10.1111/j.1365-2753.2009.01264.x" target="_blank" rel="noreferrer noopener">10.1111/j.1365-2753.2009.01264.x</a>
Format
The file format, physical medium, or dimensions of the resource
Journal Article or Conference Abstract Publication
2009
complications
computer
Delivery
Department of Internal Medicine
Diabetes
Gardner W G
General & Internal
Gil K M
Haller N A
Health Care Sciences & Services
Intervention
involvement
Journal Article or Conference Abstract Publication
Journal of evaluation in clinical practice
Knowledge
management guidelines
Medical Informatics
Medicine
mellitus
NEOMED College of Medicine
Patient
Performance
preventive services
primary care
quality
Risk
standards
Whittier F C
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
1293-1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
An entity responsible for making the resource available
Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
Adult; Female; Humans; Male; Middle Aged; Socioeconomic Factors; Aged; Chronic Disease; *Computer Simulation; Longitudinal Studies; Comorbidity; *Health Status; Artificial Intelligence; cluster analysis; Life Change Events; primary care; Artificial Intelligence; case-based modelling; comorbid depression and physical health; complexity theory; differential equations; longitudinal analysis; nonlinear dynamics; Systems Analysis; Adult Survivors of Child Abuse/statistics & numerical data; Depression/*epidemiology/*physiopathology; Health Services Research/*methods; Intimate Partner Violence/statistics & numerical data; Primary Health Care/organization & administration
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
*Computer Simulation
*Health Status
2018
Adult
Adult Survivors of Child Abuse/statistics & numerical data
Aged
Artificial Intelligence
case-based modelling
Castellani Brian
Chronic Disease
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Depression/*epidemiology/*physiopathology
differential equations
Female
Griffiths Frances
Gunn Jane
Health Services Research/*methods
Humans
Intimate Partner Violence/statistics & numerical data
Journal of evaluation in clinical practice
Life Change Events
longitudinal analysis
Longitudinal Studies
Male
Middle Aged
Nonlinear Dynamics
primary care
Primary Health Care/organization & administration
Rajaram Rajeev
Socioeconomic Factors
Systems Analysis
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Pages
1293–1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
An entity responsible for making the resource available
Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
artificial intelligence; case-based modelling; Child Abuse; cluster analysis; comorbid depression and physical health; Comorbidity; complexity theory; Computer Simulation; Depression – Therapy; differential equations; Health Status; Human; Intimate Partner Violence; longitudinal analysis; Models; nonlinear dynamics; primary care; Primary Health Care; Prospective Studies; Questionnaires; Research Personnel; Scales; Theoretical
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
2018
Artificial Intelligence
case-based modelling
Castellani Brian
Child Abuse
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Computer Simulation
Depression – Therapy
differential equations
Griffiths Frances
Gunn Jane
Health Status
Human
Intimate Partner Violence
Journal of evaluation in clinical practice
longitudinal analysis
Models
Nonlinear Dynamics
primary care
Primary Health Care
Prospective Studies
Questionnaires
Rajaram Rajeev
Research Personnel
Scales
Theoretical