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Attention Score in Context
| Title |
Improvement of the quality payment program by improving data reporting process: an action research
|
|---|---|
| Published in |
BMC Health Services Research, September 2018
|
| DOI | 10.1186/s12913-018-3472-4 |
| Pubmed ID | |
| Authors |
Shabnam Iezadi, Jafar Sadegh Tabrizi, Akbar Ghiasi, Mostafa Farahbakhsh, Kamal Gholipour |
| Abstract |
Successful implementation of pay-for-quality (P4Q) programs mostly depends upon a valid, timely, and reliable data about quality measures generated by providers, and interpreted by payers. The aim of this study was to establish a data reporting method for P4Q program through an action research. Qualitative method was used to align theory with action through a three-cycle action research. The study was conducted in September 15, 2015 to March 15, 2017, in East-Azerbaijan, Iran. The purposeful sampling was used to select participants. The participants included healthcare providers, staff in district health centers (DHC), experts, and managers in the provincial primary health center (PPHC). Data was collected by interviews, focus group discussions, and expert panels. Content analysis was used to synthesize the data. In each step, decisions about data reporting methods were made through a consensus of expert panel members. The most important dimensions of data reporting method were data entry and accuracy, data reporting, data analysis and interpretations, the flexibility of method, and training. By establishment of an online data reporting system for the P4Q program, a major improvement was observed in the documentation of performance data, the satisfaction of health care providers and staff (e.g. either in DHCs or PPHC), improvement of the P4Q program and acceptance of the P4Q program by providers. Following the present study, the online system was expanded in Iran's public health system for data collection and estimating the amount of incentive payments in P4Q program. Moreover, more improvements were achieved by linking the system to EMRs and also, providing automated feedback to providers about their own performance. A web-based computerized system with the capability of linking medical record and also its ability to provide feedback to healthcare providers was identified as an appropriate method of data reporting in the P4Q program from the viewpoints of participants in this study. |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 15% |
| Student > Postgraduate | 7 | 10% |
| Other | 5 | 7% |
| Student > Doctoral Student | 5 | 7% |
| Student > Bachelor | 5 | 7% |
| Other | 14 | 21% |
| Unknown | 21 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 10 | 15% |
| Medicine and Dentistry | 9 | 13% |
| Social Sciences | 7 | 10% |
| Engineering | 4 | 6% |
| Economics, Econometrics and Finance | 3 | 4% |
| Other | 12 | 18% |
| Unknown | 22 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 September 2018.
All research outputs
#5,832,866
of 23,102,082 outputs
Outputs from BMC Health Services Research
#2,581
of 7,743 outputs
Outputs of similar age
#101,455
of 336,142 outputs
Outputs of similar age from BMC Health Services Research
#88
of 177 outputs
Altmetric has tracked 23,102,082 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,743 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,142 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.