Dataset Information

Improvement in neonatal intensive care in Northern Ireland through sharing of audit data.

ABSTRACT:

Problem

Ten percent of infants born will require admission to a neonatal facility. Coordinated activity to monitor and improve the quality of care for this high risk, high cost group of infants is considered a high priority. At the time of initiation of this project no system for collection and analysis of neonatal data existed in Northern Ireland.

Design

In 1994 an ongoing prospective centralised data collection system was implemented to facilitate quality improvement and research in neonatal care. We aim to ascertain if there has been a demonstrable improvement in the quality of care provided since the initiation of this system.

Setting

All nine Northern Ireland neonatal intensive care units returned prospectively collected socioeconomic, obstetric and neonatal episode data.

Key measures for improvement

Achievement of the agreed quality indicators relating to transfer patterns, thermoregulation, antenatal steroid administration, and timing of administration of surfactant during the period 1 April 1999 to 31 March 2000 were compared with data for the period 1 April 1994 to 31 March 1996.

Strategies for change

Monitoring included audit and annual feedback of timely clear and relevant data where results were provided confidentially as standardised reports, together with anonymised comparisons with other similar sized units. Draft recommendations were made at regional level and units were asked to adopt finalized consensus guidelines at the local level and to implement changes to clinical practice.

Effects of change

The proportion of transfers taking place in utero increased from 26% to 42% and antenatal steroid administration from 68% to 82%. Normothermia on first admission improved from 66% to 71% for inborn infants. The proportion of infants receiving surfactant where the first dose was given within an hour of birth increased from 13% to 66%.

Lessons learnt

A multi-professional regional care network can facilitate the development of agreed standards and a culture of regular evaluation leading to quality improvement.

SUBMITTER: Jenkins J

PROVIDER: S-EPMC1744012 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improvement in neonatal intensive care in Northern Ireland through sharing of audit data.

Jenkins J J Alderdice F F McCall E E

Quality & safety in health care 20050601 3

<h4>Problem</h4>Ten percent of infants born will require admission to a neonatal facility. Coordinated activity to monitor and improve the quality of care for this high risk, high cost group of infants is considered a high priority. At the time of initiation of this project no system for collection and analysis of neonatal data existed in Northern Ireland.<h4>Design</h4>In 1994 an ongoing prospective centralised data collection system was implemented to facilitate quality improvement and researc ...[more]

PMID: 15933318

Similar Datasets

Project description:BACKGROUND:?In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). METHODS:?The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. RESULTS:?The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p?=?0.03). CONCLUSION:?Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Project description:BackgroundClinical trial transparency is important to participants, trialists, publishers, and regulators, and there have been recent major policy changes by the pharmaceutical industry regarding clinical study data sharing. However, it is unknown if these changes are enabling independent researchers to access participant-level data from prominent contemporary clinical trials sponsored by the pharmaceutical industry 2 years after publication of the primary results.Main textPubMed and ClinicalTrials.gov were searched to identify clinical trials of medicines sponsored by the pharmaceutical industry and first published between 1 July 2015 and 31 December 2015 in the top 10 general and internal medical journals by impact factor. For each clinical trial, the eligibility of independent researchers to request participant-level data was identified via the sponsor having a data sharing policy/process and a positive response to an enquiry. Fifty-six publications reporting on 61 industry-sponsored clinical trials were identified, of which 32 (52%) had a public data sharing policy/process and 9 (15%) were confirmed eligible for data sharing. Industry sponsors within the top 25 by global sales were more likely to have a data sharing policy (93% vs 10%), and there was a trend towards increased data sharing eligibility (23% vs 4%). Twenty-six studies were explicitly confirmed as ineligible for data sharing. The two most common data sharing policy conditions that prevented sharing of data for published results were the exclusion of studies that had ongoing follow-up of the published results and the exclusion of studies of medicines that have not yet achieved regulatory approval in the USA and the European Union.ConclusionsFifteen percent of the sampled clinical trials were available for data sharing 2 years after publication of primary results of the trial. Key issues limiting data sharing include a large proportion of industry sponsors who do not have a data sharing policy/process, and data sharing policy conditions that exclude access on the basis of ongoing follow-up and regulatory activity.

Project description:BackgroundThere is considerable evidence about the effectiveness of audit coupled with feedback, although few audit with feedback interventions have been conducted in long-term care (LTC) settings to date. In general, the effects have been found to be modest at best, although in settings where there has been little history of audit and feedback, the effects may be greater, at least initially. The primary purpose of the Data for Improvement and Clinical Excellence (DICE) Long-Term Care project is to assess the effects of an audit with feedback intervention delivered monthly over 13 months in four LTC facilities. The research questions we addressed are:1. What effects do feedback reports have on processes and outcomes over time?2. How do different provider groups in LTC and home care respond to feedback reports based on data targeted at improving quality of care?Methods/designThe research team conducting this study comprises researchers and decision makers in continuing care in the province of Alberta, Canada. The intervention consists of monthly feedback reports in nine LTC units in four facilities in Edmonton, Alberta. Data for the feedback reports comes from the Resident Assessment Instrument Minimum Data Set (RAI) version 2.0, a standardized instrument mandated for use in LTC facilities throughout Alberta. Feedback reports consist of one page, front and back, presenting both graphic and textual information. Reports are delivered to all staff working in the four LTC facilities. The primary evaluation uses a controlled interrupted time series design both adjusted and unadjusted for covariates. The concurrent process evaluation uses observation and self-report to assess uptake of the feedback reports. Following the project phase described in this protocol, a similar intervention will be conducted in home care settings in Alberta. Depending on project findings, if they are judged useful by decision makers participating in this research team, we plan dissemination and spread of the feedback report approach throughout Alberta.

Dataset Information

Improvement in neonatal intensive care in Northern Ireland through sharing of audit data.

Problem

Design

Setting

Key measures for improvement

Strategies for change

Effects of change

Lessons learnt

Publications

Improvement in neonatal intensive care in Northern Ireland through sharing of audit data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure