Guidance for Writing IHI Scientific Symposium Abstracts
Overview
The IHI Scientific Symposium aims to attract the best work in the science of improvement in health and health care. The symposium features a mix of keynote presentations, interactive methods sessions on new and foundational scientific approaches to improvement, and oral presentations and poster presentations featuring both applied improvement and improvement methods work. Top rated abstracts will be published in BMJ Open Quality (abstracts from 2020 are published here).
We invite abstract submissions for oral and poster presentations that showcase either:
Applied Improvement: Applications of scientific methods to health and health care improvement
Improvement Methods: developing the methodological basis of improving health and health care
Abstracts that focus on one or both of this year’s symposium themes are encouraged but not required. We are a conference focused on learning – what worked, what did not, and what can others learn from those lessons and apply to their work? Having a strong underlying theory to guide the learning – whether or not something “worked” or achieved a positive or negative result – is especially important.
Below, we include the criteria that our abstract reviewers use as well as some considerations for presenting quantitative data. We welcome quantitative, qualitative, and mixed methods work.
Review Criteria
The abstract reviewers for the IHI Scientific Symposium will use the table below to guide their review of your abstract. When writing your abstract, we recommend that you consider the rating criteria specified in the table. In addition, please follow the suggestions in the section titled, “Presentation of Quantitative Data.”
Focus of the Abstract | ||
Rating | Applied Improvement For abstracts that focus on applying or teaching approaches to improve health or health care | Improvement Methods For abstracts that focus on developing the methodological basis of improving health and health care |
1 | Unclear or does not illustrate appropriate application of QI to Health Services Delivery | Unclear or does not add anything new to the fields of QI or health research |
2 | Limited application of QI methods to health services delivery. For example: Abstract shows some good design principles, but with significant weaknesses in analysis or validity, or does not have sufficient data, or shows results that do not add to the existing knowledge base. | Little importance to the field. For example: A new application or innovative modification of existing designs, approaches, methods or tools, with limited description of the approach. Limited or no description of applicability or assessment criteria, such as effectiveness, validity or reliability. |
3 | Important application of QI methods to health services delivery. For example: Clear aims, some description of the methods, sufficient data and adequate analysis (quantitative and/or qualitative), but somewhat limited significance in terms of contributions to knowledge. | Important to the field. For example: A clearly described new application or innovative modification of existing designs, approaches, methods or tools. Some description of applicability or assessment criteria, such as effectiveness, validity or reliability. |
4 | Very important application of QI methods to health services delivery. For example: Clear aims, clear description of the methods, sufficient data, appropriate analysis (quantitative and/or qualitative) and some description of what was learned and how it may be applied, with important implications for knowledge. | Very important to the field. For example: A clearly described new application or innovative modification of existing designs, approaches, methods or tools. Adequate description of applicability assessment criteria, such as effectiveness, validity or reliability. |
5 | Excellent application of QI methods applied to health services delivery. For example: Clear aims, clear description of the methods, sufficient data, appropriate analysis (quantitative and/or qualitative) and a thorough description of what was learned and how it may be applied, plus highly significant contributions to knowledge. | Major importance to the field. For example: A clearly described new application or innovative modification of existing designs, approaches, methods or tools. Clearly described. Excellent description of applicability assessment criteria, such as effectiveness, validity or reliability. |
Presentation of Quantitative Data
Abstracts will have a better chance of being accepted if the quantitative data presented are clear, concise and follow general statistical principals. We also welcome qualitative data and mixed methods approaches. For improvement studies, we expect to see time-ordered data. However, we understand that for some studies, summative or cross-sectional data is appropriate. Below, we provide guidance for presenting quantitative data. These are suggestions, not requirements – there certainly will be situations in which other approaches to data presentation will be appropriate.
Stratified Data
In addition to overall results, we strongly encourage authors to assess whether impact was equitable across groups, and to report results stratified by race, ethnicity, language, gender, and/or other relevant demographic indicators (e.g., geographic region). For example, this may take the form of small-multiple run and control charts or a table with results stratified by race and ethnicity.
Time-ordered Data
Run charts: If you present run charts, state the criteria you are using to indicate whether the data is signaling a change over time. The linked resource includes more details on Run Chart rules.
Control Charts: If you present control charts, provide the type of chart you used, and what method you used to indicate “control limits.” The linked resource includes more details on control charts.
Time series analysis: If you use a time-series analysis or a related regression approach, provide a brief description of the approach and provide relevant estimates and their associated confidence intervals.
Summarizing Data
For continuous or ordinal data, include a measure of central tendency (e.g., mean, median or mode). Also provide an indication of the distribution of the data. For example, when presenting a mean, include a standard deviation or standard error, when presenting a median, include the appropriate percentiles (these could include the 5th/95th percentiles, 25th/75thpercentiles, or minimum/maximum).
For example:
- The mean birthweight of the 232 infants born in St Jane’s Hospital in 2012 was 3256.6g (standard deviation = 245.6g).
- The median age of 216 mothers giving birth to infants born in St Jane’s Hospital in 2012 was 32 (25th, 75th percentiles 18, 38).
- For categorical data, include a numerator and a denominator. If summarizing data across groups, express the data as a percentage.
For example:
- Of 232 infants, born in St Jane’s Hospital in 2012, 120 (51.7%) were female.
- Of 232 infants, born in St Jane’s Hospital in 2012, 20 (8.6%) were born at or before 31 weeks gestation, 38 (16.4%) were born between 32 and 38 weeks gestation and 174 (75.0%) were born at 39 or more weeks gestation.
Estimating differences between groups
When providing an estimate of a difference between two or more groups, provide an estimate of the difference between the groups and a confidence interval. Providing the estimate and confidence interval provides a clearer understanding to the reader of the actual effect and the likely precision around the estimate than a p-value does.
For example:
- Infants born in St Jane’s Hospital in 2012 weighed on average 245.2g (95% CI 223.2g, 267.6g) more than those born in 2010.
Decimal places
For all types of data summary and estimates, summarize data to meaningful decimal places. As a rule of thumb, do not use more than one decimal place in addition to the actual unit the initial measurement was recorded in.
For example:
- Assuming birthweights are measured to the nearest whole gram (g): The mean birthweight of the 232 infants born in St Jane’s Hospital in 2012 was 3256.6g (standard deviation = 245.6g) -- not: The mean birthweight of the 232 infants born in St Jane’s Hospital in 2012 was 3256.5678g, (245.5431g).