Comparison of computerized surveillance and manual chart review for adverse events

Tinoco, Aldo; Evans, R. Scott; Staes, Catherine J.; Lloyd, James F.; Rothschild, Jeffrey M.; Haug, Peter J.

doi:10.1136/amiajnl-2011-000187

Cited by 50 publications

(54 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More recently, Tinoco and colleagues reported that a computerised surveillance system detected more hospital acquired infections than trigger tool based screening, but a similar number of ADEs 19. Importantly, the study found that only 26% of hospital acquired infections and 3% of ADEs were detected by both methods 19. Our study provides additional support that computer facilitated screening may complement the traditional trigger tool approach and suggests that the rate of hospital AEs may be even higher than previously reported.…”

Section: Discussionsupporting

confidence: 70%

“…In a study focusing on ADEs, Jha and colleagues reported that a computer based method detected 45% of events, trigger tool based screening detected 65% and only 12% were detected by both methods 14. More recently, Tinoco and colleagues reported that a computerised surveillance system detected more hospital acquired infections than trigger tool based screening, but a similar number of ADEs 19. Importantly, the study found that only 26% of hospital acquired infections and 3% of ADEs were detected by both methods 19.…”

Section: Discussionmentioning

confidence: 99%

“…Several teams of researchers have tested automated screens to identify adverse drug events (ADEs) using sudden medication stop orders, antidote ordering and certain abnormal laboratory values 13–17. However, very little research has evaluated the potential for automated screens to identify non-drug related AEs18 or the use of a combination of information systems to facilitate the identification of AEs 19. Many healthcare organisations are implementing business intelligence tools, such as data warehouses, to identify, extract and analyse data to assist operational, clinical and research efforts 20–24.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events

et al. 2012

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Until recently, most studies sought to establish harm rates for benchmarking purposes over large populations with suggestions that between 3% and 17% of patients experience an AE during a hospital admission 22. Most of these studies used retrospective, unstructured case note reviews which are labour-intensive, costly and impractical for the routine monitoring of harm 23. Trigger tool methodology is accepted as one of the ways to measure harm in a way that allows local learning,1 3 2 Trigger tools have also been reported to provide consistent, reliable and relevant data at low cost,2 3 although the cost may vary between different hospitals.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and severity of patient harm in a sample of UK-hospitalised children detected by the Paediatric Trigger Tool

et al. 2014

View full text Add to dashboard Cite

The measurement and examination of adverse events (AEs) that occur in children during hospital admissions is essential if we are to prevent, reduce or ameliorate the harm experienced. The UK Paediatric Trigger Tool (UKPTT) is a method of retrospective case note review that measures harm in hospitalised children.ObjectivesTo examine the harm resulting from the processes of healthcare in hospitalised children from centres providing data to the National Health Service (NHS) Institute UKPTT data portal, to understand the positive predictive values of triggers and to make recommendations for the further development of the trigger tool.Setting25 hospitals across the UK, including secondary, tertiary and quaternary paediatric centres.ParticipantsRandomly selected children who were admitted to hospital for longer than 24 h.Outcome measuresThe primary outcome measure was the rate of harm (the percentage of children experiencing one or more AEs during a hospital admission). Secondary measures were the severity of harm and performance of triggers.ResultsData from 3992 patient admissions were reviewed across the hospitals and submitted to the trigger tool portal from February 2008 to November 2011. At least one AE was reported for 567 (14.2%) patients, with 211 (5.3%) experiencing more than one event. There were 1001 AEs identified. Where harm occurred, it was considered temporary for 923 (92.2%) AEs; however, 43 (4.3%) AEs resulted in the need for life-sustaining interventions, 18 (1.8%) AEs led to permanent harm and for 17 children (1.7% of AEs) the AE was believed to have contributed to death.ConclusionsThere is a significant, measurable level of harm experienced by children admitted to hospitals in the UK. While most of this harm is temporary, some of it is serious. The UKPTT offers organisations the means to measure and examine the AEs occurring in their hospital in order to reduce harm.

show abstract

“…In research, manual chart review is often still considered the “gold standard,” 8–12 though this method is time consuming and expensive and thus can only be applied to small numbers of patients. As electronic systems matured, administrative health insurance claims were increasingly used to capture information on larger populations.…”

Section: Introductionmentioning

confidence: 99%

Measuring Preventive Care Delivery

Bailey

Heintzman

Hoopes

et al. 2016

American Journal of Preventive Medicine

View full text Add to dashboard Cite

Introduction Preventive care delivery is an important quality outcome, and electronic data reports are increasingly being used to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness. Methods This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of correspondence between manual chart review and two automated sources (electronic health records [EHRs] and Medicaid claims), comparing documentation of orders for and receipt of ten preventive services (n=150 patients/service). Data were analyzed in 2015. Results Using manual chart review as the gold standard, automated EHR extraction showed near-perfect to perfect agreement (κ=0.96–1.0) for services received within the primary care setting (e.g., BMI, blood pressure). Receipt of breast and colorectal cancer screenings, services commonly referred out, showed moderate (κ=0.42) to substantial (κ=0.62) agreement, respectively. Automated EHR extraction showed near-perfect agreement (κ=0.83–0.97) for documentation of ordered services. Medicaid claims showed near-perfect agreement (κ=0.87) for hyperlipidemia and diabetes screening, and substantial agreement (κ=0.67–0.80) for receipt of breast, cervical, and colorectal cancer screenings, and influenza vaccination. Claims showed moderate agreement (κ=0.59) for chlamydia screening receipt. Medicaid claims did not capture ordered or unbilled services. Conclusions Findings suggest that automated EHR and claims data provide valid sources for measuring receipt of most preventive services; however, ordered and unbilled services were primarily captured via EHR data and completed referrals were more often documented in claims data.

show abstract

Comparison of computerized surveillance and manual chart review for adverse events

Abstract: The effect that information source has on different surveillance methods depends on the type of AE. Integrating information from physician narratives with CSS using natural language processing would improve the detection of ADEs more than HAIs.

Cited by 50 publications

References 30 publications

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events

Prevalence and severity of patient harm in a sample of UK-hospitalised children detected by the Paediatric Trigger Tool

Measuring Preventive Care Delivery

Contact Info

Product

Resources

About