Making audit actionable: an example algorithm for blood pressure management in chronic kidney disease.

Benjamin Brown, Richard Williams, Matthew Sperrin, Timothy Frank, John Ainsworth, Iain Buchan

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing 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

3 citing papers in PubMed.

Multi-method laboratory user evaluation of an actionable clinical performance information system: Implications for usability and patient safety.Journal of biomedical informatics · 2018
Article
Low salivary cortisol levels in patients with rheumatoid arthritis exposed to oral glucocorticoids: a cross-sectional study set within UK electronic health records.RMD open · 2018
Article
Interface design recommendations for computerised clinical audit and feedback: Hybrid usability evidence from a research-led system.International journal of medical informatics · 2016
Article

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

6 authors.

Benjamin BrownCentre for Health Informatics, University of Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, UK.

Richard WilliamsCentre for Health Informatics, University of Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, UK.

Matthew SperrinCentre for Health Informatics, University of Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, UK.

Timothy FrankCentre for Health Informatics, University of Manchester, UK.

John AinsworthCentre for Health Informatics, University of Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, UK.

Iain BuchanCentre for Health Informatics, University of Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, UK.

Funding

Medical Research Council MC_PC_13042Medical Research Council MR/K006665/1

6 · The paper itself

Abstract

Despite widespread use of clinical guidelines, actual care often falls short of ideal standards. Electronic health records (EHR) can be analyzed to provide information on how to improve care, but this is seldom done in sufficient detail to guide specific action. We developed an algorithm to provide practical, actionable information for care quality improvement using blood pressure (BP) management in chronic kidney disease (CKD) as an exemplar. We used UK clinical guidelines and EHR data from 440 patients in Salford (UK) to develop the algorithm. We then applied it to 532,409 individual patient records, identifying 11,097 CKD patients, 3,766 (34%) of which showed room for improvement in their care: either through medication optimization or better BP monitoring. Manual record reviews to evaluate accuracy indicated a positive-predictive value of 90%. Such algorithms could help improve the management of chronic conditions by providing the missing link between clinical audit and decision support.

Indexed as

AlgorithmsElectronic Health RecordsAntihypertensive AgentsBlood Pressure DeterminationDisease ManagementHumansHypertensionPatient ComplianceRenal Insufficiency, ChronicAntihypertensive Agents

Identifiers

PMID25954337

PMCPMC4419945

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.