Diagnostic assessments of spirometry and medical history data by respiratory specialists supporting primary care: are they reliable?

For the past several years, experimental studies have been undertaken at the Mayo Clinic to evaluate the feasibility of utilizing electronic data processing to handle medical information, especially the medical information which makes up a medical record. We have experimented with automated techniques for collecting and storing medical-history data, specifically with techniques for computer generation and processing of health questionnaires, for computer-controlled administration of health questionnaires, and for computer-controlled entry and retrieval of medical-history data directly by physicians in ordinary English language by use of a video-screen and light-pen computer terminal.The questionnaire studies are concerned with ways of entering into computer storage medical-history data obtained from patients without physician involvement; the video-screen studies are concerned with entry into computer storage of medical-history data obtained by physicians in their interview with the patient. The paper describes our experiences in these studies.

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Diagnostic Classification of Medical History Data

Computers and Mathematical Models in Medicine - Lecture Notes in Medical Informatics ◽

10.1007/978-3-642-93159-8_14 ◽

1981 ◽

pp. 214-231

Author(s):

J. R. Moehr ◽

W. Raufmann ◽

J. Juhnke

Keyword(s):

Medical History ◽

Diagnostic Classification ◽

History Data

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Detection and Prediction of Incident Alzheimer Dementia over a 10-Year or Longer Medical History: A Population-Based Study in Primary Care

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000509379 ◽

2020 ◽

pp. 1-6

Author(s):

Giulia Grande ◽

Davide L. Vetrano ◽

Francesco Mazzoleni ◽

Valeria Lovato ◽

Mario Pata ◽

...

Keyword(s):

Primary Care ◽

Medical History ◽

General Practitioners ◽

Motor Behavior ◽

Early Recognition ◽

Conditional Logistic Regression ◽

Control Analysis ◽

Conditional Logistic Regression Model ◽

Alzheimer Dementia ◽

Incident Cases

Background: Despite the crucial role played by general practitioners in the identification and care of people with cognitive impairment, few data are available on how they may improve the early recognition of patients with Alzheimer dementia (AD), especially those with long (i.e., 10 years and longer) medical history. Aims: To investigate the occurrence and the predictors of AD during a 10-year or longer period prior AD diagnosis in primary care patients aged 60 years or older. Materials and Methods: A cohort study with a nested case-control analysis has been conducted. Data were extracted from the Italian Health Search Database (HSD), an Italian database with primary care data. AD cases have been defined in accordance with the International Classification of Diseases, ninth edition (ICD-9-CM) codes and coupled with the use of anti-dementia drugs. Prevalence and incidence rates of AD have been calculated. To test the association between candidate predictors, being identified in a minimum period of 10 years, and incident cases of AD, we used a multivariate conditional logistic regression model. Results: As recorded in the primary care database, AD prevalence among patients aged 60 years or older was 0.8% during 2016, reaching 2.4% among nonagenarians. Overall, 1,889 incident cases of AD have been identified, with an incidence rate as high as 0.09% person-year. Compared with 18,890 matched controls, history of hallucinations, agitation, anxiety, aberrant motor behavior, and memory deficits were positively associated with higher odds of AD (p < 0.001 for all) diagnosis. A previous diagnosis of depression and diabetes and the use of low-dose aspirin and non-steroidal anti-inflammatory drugs were associated with higher odds of AD (p < 0.05 for all). Conclusion: Our findings show that, in accordance with primary care records, 1% of patients aged 60 years and older have a diagnosis of AD, with an incident AD diagnosis of 0.1% per year. AD is often under-reported in primary care settings; yet, several predictors identified in this study may support general practitioners to early identify patients at risk of AD.

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Experiences with the Use of Automation for Collecting and Recording Medical-History Data

Methods of Information in Medicine ◽

10.1055/s-0038-1636684 ◽

1969 ◽

Vol 8 (02) ◽

pp. 53-59

Author(s):

John G. Mayne

Keyword(s):

Medical History ◽

English Language ◽

Medical Information ◽

Experimental Studies ◽

Video Screen ◽

History Data ◽

Computer Storage ◽

Computer Controlled ◽

Questionnaire Studies ◽

Health Questionnaires

For the past several years, experimental studies have been undertaken at the Mayo Clinic to evaluate the feasibility of utilizing electronic data processing to handle medical information, especially the medical information which makes up a medical record. We have experimented with automated techni- ques for collecting and storing medical-history data, specifically with techniques for computer generation and processing of health questionnaires, for computer-controlled administration of health questionnaires, and for computer-controlled entry and retrieval of medical-history data directly by physicians in ordinary English language by use of a video-screen and light-pen computer terminal. The questionnaire studies are concerned with ways of entering into computer storage medical-history data obtained from patients without physician involvement; the video-screen studies are concerned with entry into computer storage of medical-history data obtained by physicians in their interview with the patient. The paper describes our experiences in these studies.

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Human- Versus Machine Learning–Based Triage Using Digitalized Patient Histories in Primary Care: Comparative Study (Preprint)

10.2196/preprints.18930 ◽

2020 ◽

Author(s):

Artin Entezarjou ◽

Anna-Karin Edstedt Bonamy ◽

Simon Benjaminsson ◽

Pawel Herman ◽

Patrik Midlöv

Keyword(s):

Machine Learning ◽

Primary Care ◽

Physical Examination ◽

Medical History ◽

Primary Care Physicians ◽

Interrater Reliability ◽

Reference Group ◽

Majority Vote ◽

Medical Histories ◽

Using Data

BACKGROUND Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage). OBJECTIVE The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method. METHODS After testing several models, a naïve Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination. The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen κ (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). RESULTS Interrater reliability as measured by Cohen κ was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination. No specific features linked to the model’s triage decision could be identified. Between physicians within the panel, Cohen κ was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen κ of 0.55. CONCLUSIONS Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.

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A Human-Organisation-Technology (HOT) perspective on patients’ experiences of a chat- and automated medical history-taking service in primary healthcare: qualitative study (Preprint)

10.2196/preprints.29868 ◽

2021 ◽

Author(s):

Evalill Nilsson ◽

Annette Sverker ◽

Preben Bendtsen ◽

Ann Catrine Eldh

Keyword(s):

Primary Care ◽

Qualitative Study ◽

Medical History ◽

Healthcare Professionals ◽

Medical History Taking ◽

History Taking ◽

Single Issue ◽

User Experience Design ◽

Advantages And Disadvantages ◽

Catchment Areas

BACKGROUND Worldwide, the use of e-consultations in healthcare is progressing fast. So far, studies on the advantages and disadvantages of e-consultations in the form of chat services for all enquiries in primary care have focused on the perspective of the healthcare professionals rather than the end-users (patients). OBJECTIVE To explore patients´ experiences of using a chat- and automated medical history-taking service in Swedish regular tax-based not-for-profit primary care. METHODS In this qualitative study, 25 individual interviews were conducted with patients in the catchment areas of five primary care centres (PCCs) in Sweden that tested a chat- and automated medical history-taking service for all kinds of patient enquiries. The semi-structured interviews were transcribed verbatim prior to content analysis, using inductive and deductive strategies, the latter including an unconstrained matrix of Human, Organisation and Technology (HOT) perspectives. RESULTS The service provided an easily managed way for patients to make written contact, which was considered beneficial for some patients and issues, but less suitable for others (like acute or more complex cases). The automated medical history-taking service was perceived as having potential, but still derived from what healthcare professionals need to know and how they address and communicate health and healthcare issues. Technical skills were not considered as necessary for a mobile phone chat as for handling a computer, for example, but patients still expressed concern for people with less digital literacy. The opportunity for patients to take their time and reflect before answering questions from the healthcare professionals was found to be stress reducing and error preventing, and patients speculated that it might be the same for the healthcare professionals on the other end of the system. Patients appreciated the ability to have a conversation from almost anywhere, even from places not suitable for telephone calls. The asynchronicity of the chat service let the patients take more control of the conversation and initiate a chat at any time at their own convenience, but it could also lead to lengthy conversations where a single issue in the worst cases could take days to close. The opportunity to upload photographs made some visits to the PCC redundant which would otherwise have been necessary if a telephone service had been used, saving patients both time and money. CONCLUSIONS Patients generally had a positive attitude towards e-consultations in primary care and were generally pleased with the prospects of the digital tool tested, somewhat more with the actual chat than with the automated history-taking system preceding the chat. While patients expect their PCC to offer a range of different means of communication, the HOT analysis revealed a need for a more extensive (end) user-experience design in the further development of the PCC chat service.

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