Evidence map›Paper›PMID 18812313›Full record

ArticleJournal of medical Internet research2008

The ALFA (Activity Log Files Aggregation) toolkit: a method for precise observation of the consultation.

Simon de Lusignan, Pushpa Kumarapeli, Tom Chan, Bernhard Pflug, Jeremy van Vlymen, Beryl Jones, George K Freeman

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Delivery of Supported Self-Management in Primary Care Asthma Reviews: Insights From the IMPHealth expectations : an international journal of public participation in health care and health policy · 2024
    Observational
  2. Barriers and Facilitators to the Initiation of Injectable Therapies for Type 2 Diabetes Mellitus: A Mixed Methods Study.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Simon de LusignanBiomedical Informatics, Division of Community Health Sciences, St. George's University of London, London SW17 0RE, UK. slusigna@sgul.ac.uk
Pushpa Kumarapeli
Tom Chan
Bernhard Pflug
Jeremy van Vlymen
Beryl Jones
George K Freeman

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a lack of tools to evaluate and compare Electronic patient record (EPR) systems to inform a rational choice or development agenda.

objectiveTo develop a tool kit to measure the impact of different EPR system features on the consultation.

methodsWe first developed a specification to overcome the limitations of existing methods. We divided this into work packages: (1) developing a method to display multichannel video of the consultation; (2) code and measure activities, including computer use and verbal interactions; (3) automate the capture of nonverbal interactions; (4) aggregate multiple observations into a single navigable output; and (5) produce an output interpretable by software developers. We piloted this method by filming live consultations (n = 22) by 4 general practitioners (GPs) using different EPR systems. We compared the time taken and variations during coded data entry, prescribing, and blood pressure (BP) recording. We used nonparametric tests to make statistical comparisons. We contrasted methods of BP recording using Unified Modeling Language (UML) sequence diagrams.

resultsWe found that 4 channels of video were optimal. We identified an existing application for manual coding of video output. We developed in-house tools for capturing use of keyboard and mouse and to time stamp speech. The transcript is then typed within this time stamp. Although we managed to capture body language using pattern recognition software, we were unable to use this data quantitatively. We loaded these observational outputs into our aggregation tool, which allows simultaneous navigation and viewing of multiple files. This also creates a single exportable file in XML format, which we used to develop UML sequence diagrams. In our pilot, the GP using the EMIS LV (Egton Medical Information Systems Limited, Leeds, UK) system took the longest time to code data (mean 11.5 s, 95% CI 8.7-14.2). Nonparametric comparison of EMIS LV with the other systems showed a significant difference, with EMIS PCS (Egton Medical Information Systems Limited, Leeds, UK) (P = .007), iSoft Synergy (iSOFT, Banbury, UK) (P = .014), and INPS Vision (INPS, London, UK) (P = .006) facilitating faster coding. In contrast, prescribing was fastest with EMIS LV (mean 23.7 s, 95% CI 20.5-26.8), but nonparametric comparison showed no statistically significant difference. UML sequence diagrams showed that the simplest BP recording interface was not the easiest to use, as users spent longer navigating or looking up previous blood pressures separately. Complex interfaces with free-text boxes left clinicians unsure of what to add.

conclusionsThe ALFA method allows the precise observation of the clinical consultation. It enables rigorous comparison of core elements of EPR systems. Pilot data suggests its capacity to demonstrate differences between systems. Its outputs could provide the evidence base for making more objective choices between systems.

Indexed as

Attitude to ComputersComputersFamily PracticeHumansMedical Records Systems, ComputerizedProgramming LanguagesReferral and ConsultationSensitivity and SpecificitySoftwareUser-Computer Interface

Identifiers

PMID18812313
PMCPMC2629369

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.