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New Zealand Quality of Healthcare Study

2021· report· en· W6906489628 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Auckland Data Repository · 2021
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Health careAdverse effectMedical recordQuality (philosophy)Sample (material)Public healthPatient safetyQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Data accessTo discuss accessing the data from this study, please contact Professor Peter Davis. PublicationsFor information on the publications from this study, see https://www.auckland.ac.nz/en/arts/our-research/research-institutes-centres-groups/compass/surveys/nzqhs.html The main report is still available at https://www.health.govt.nz/system/files/documents/publications/adverseevents.pdf AbstractThe objective was to assess the occurrence, impact and preventability of adverse events recorded in New Zealand public hospitals. A two-stage retrospective review was carried out on 6,579 medical records. These were selected by systematic list sample from admissions for 1998 occurring in 13 public hospitals throughout New Zealand providing acute care and with over 100 beds, excluding specialist institutions. Following initial screening, medical records were subject to structured implicit review (that is, the guided exercise of professional judgement) by a team of trained medical officers, using a standardised protocol. The information available in the sampled medical records was of a quality that permitted the adequate identification and analysis of adverse events. The processes and instruments used in comparator studies internationally were applied in the New Zealand setting with little difficulty. Reliability and validity measures displayed only moderate levels of agreement, however. Analysis of the 850 adverse events identified revealed a distribution, impact, and clinical context comparable with other studies. Adverse events (which may have occurred either within or outside public hospitals) were associated with 12.9 percent of admissions. Approximately 35 percent of adverse events were classified as highly preventable. Although less than 15 percent of adverse events resulted in permanent disability or death, an average of over nine days per event was added to hospital stay. Nearly a fifth of events originated from outside public hospitals, only a quarter of which arose in another institutional context. Patient age was an important risk factor for an adverse event. There were distinct patterns according to clinical and administrative context. Systems errors featured prominently in the analysis of areas for the prevention of recurrence. This study provides the base parameters necessary to inform our understanding of patient safety and the quality of care in New Zealand public hospitals. These data have important managerial and clinical implications. Further work could be done on subgroups of patients and on the clinical detail available in the data. The investigation provides a baseline for more targeted studies and for quality improvement interventions. It also points to the importance of similar research on the sources and characteristics of adverse events outside public hospitals. Data collectionFieldwork was conducted over the period from the beginning of July 1999 to the end of May 2000, by a team of four Registered Nurse (RN) screeners and three or four Medical Officer (MO) reviewers overseen by the Project Manager. An Expert Reviewer arbitrated on discrepant judgements (where an RN and an MO disagreed) and carried out an independent review of a subsample of selected medical records. Before the commencement of data collection, fieldworkers undertook an intensive training course. Data collection took place over a period of three weeks at each hospital. Each sampled case was allocated a study identification number so that identifiers allowing linkage to hospital records could be deleted to maintain patient anonymity. SamplingA stratified two-stage cluster sample design was used. 1. A nationally representative sample of 13 was generated from the 20 public hospitals with more than 100 beds. 2. A random sample of admissions was drawn within each hospital. Sampling of hospitals followed stratification by hospital type and geographic area across New Zealand. The 3 strata were:1. Six large tertiary service facilities.2. Seven secondary service facilities with more than 300 beds.3. Seven secondary service facilities with fewer than 300 beds. The national sample comprised all six hospitals from the first stratum, probability proportional to size samples of four hospitals from the second stratum and three hospitals from the third stratum. The New Zealand Health Information Service (NZHIS) selected a random sample of 575 admissions from each of the sampled 13 hospitals for the year 1998. The selected time of admission for sampled cases signalled an index admission (the sampled admission) and provided the point of reference in adverse event (AE) determination. The sampling frame for each hospital was the list of all eligible admissions in that hospital. This number was divided by the 575 to be selected in order to come up with a systematic sampling interval, following from a random starting point between 1 and 100. This generated samples of the required size according to standard principles of systematic list sampling. In order to assess the representativeness of the sample, the distributions of key patient characteristics were compared with the patterns for all New Zealand public hospital admissions in 1998. It should be noted that the data in this study represent approximately a 1 in 100 sample of all publicly funded hospitalisations. The sample medical records were closely representative of all New Zealand public hospital admissions in a number of key demographic and clinical characteristics, including age, gender, ethnic group, discharge status and mortality. Length of stay in the sample appeared to be shorter than the average for all publicly funded hospitalisations in New Zealand for 1998. Characteristics of data collection situationRoutine quality checks were carried out to improve the quality of information gathered. During both the screening and review stages, forms were checked for completeness and adequacy. During the review stage, RN and MO discrepancies in judgement of criteria presence or AE determination were checked and if necessary forwarded for adjudication to the Expert Reviewer. At the stage of data entry from the completed forms, standard checks for invalid, out-of-range, inconsistent and missing data were used to identify errors. Where medical knowledge was necessary, the Expert Reviewer was consulted. Actions to minimise lossesIn the event of a home being unoccupied at the time of the initial visit, at least 2 other visits were made at different times of the day, in an attempt to make contact. WeightingThe cases needed to be weighted to account for unequal selection probabilities in the sample design. Each hospital was given a weight inversely proportional to its selection probability when calculating estimates of rates, proportions and means. In actuality, weighting the cases gave similar results. Standard errors also needed to be adjusted for the two-stage cluster design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.428
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.010
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.122
GPT teacher head0.349
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2021
Admission routes1
Has abstractyes

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