QI PROJECT- IMPROVING POST-GRADUATE RESEARCH PROJECTS

We, from the Department of General Medicine, at Kamineni Institute of Medical Sciences made high-quality research our top priority. Research within the field of General Medicine is crucial for advancing medical knowledge, improving patient outcomes, and implementing evidence-based medicine in practice. In our department, several research projects are currently underway, addressing a wide range of clinical issues. However, the effectiveness and efficiency of ongoing research projects can be significantly influenced by organizational and procedural frameworks within a department. Despite the commitment to high-quality research, various challenges have been identified that may hinder the successful completion and impact of these projects. The main challenge was insufficient research knowledge among post-graduates conducting the research. Their research needs improvements in terms of organizing the collected data and defining objectives and methodology to initiate with. Further improvements to obtain efficient results from the data available, and draw conclusions from the results obtained.

Our QIP (Quality improvement project) is based on the PDSA (Plan-Do-Study-Act) methodology.

What conditions indicated the need for the project?

-The inability of post-graduates to conduct an efficient research.

-Lack of research knowledge among post-graduates leading to an overall decrease in the research quality of the department.

-Problems with the validation of data collection

-Inability of post-graduates to formulate results from the data available.

-Questionable generalizability of the research projects

AIM OF THE QI PROJECT:

This QI project aims to bridge the knowledge gaps in research, among post-graduate students and improve the quality of the research conducted by them. 

This project also aims to enhance the research quality of the department.

OBJECTIVES:

To include a relevant introduction in the thesis projects

To help post-graduates conduct an effective review of the literature

To improve the stated methodology

To assess and improve the observations and results of the research project

To assess if the stated aims and objectives are met correctly by the end of the research

To train the researchers to obtain valid conclusions from the results obtained

Is it SMART: Single-focused, measurable, action-oriented, realistic, and timely?

Yes, our goals are single-focused (improving research quality), measurable (number of research parameters showing improved quality against a set standard), action-oriented (Improved research quality over time with regular QI cycles), realistic (deals with our everyday workflow), and timely (analysis of the research projects will be done as soon as they are discussed with us). 

What is the problem?

1. Characterize the problem

2. What causes are explored

How do you know it is a problem?

The baseline will be measured against a set standard. 

Analysis of research projects conducted by post-graduates shows none or very few have reached the set standards. Some apparent problems were:

-Incomplete research projects 

-Improperly stated methodology due to lack of knowledge on how to describe the methodology

-No or very minimal discussion in the form of comparison with other studies or review of literature is done.

-The table headings in the results are not clearly stated

-Poor correlation between the stated aims and objectives and the results obtained through the tables in these research projects

-No stated conclusions available

What is the solution?

-Does it cover short and long term?

-Does in consider up and downstream?

What is the plan for successful implementation?

-How will I set a target?

-How often will progress be measured?

Predictions:

-What do we expect to see and why?

-What consequences may be there?

-Could the change make something worse?

               By the time this QI project is being conducted, data collection is completed by post-graduates. So, data validation remains the main issue in moving forward. However, post-graduates were able to provide case report links, case sheets, collected images, and written case report forms with in-patient numbers on them to prove the validity and accuracy of data entries.

 RESEARCH PROJECTS CONSIDERED FOR THIS QI PROJECT:

Dr.NARSIMHA : PREDICTORS OF OUTCOMES IN PATIENTS WITH COMPLEX DIABETES

Dr. PAVAN KUMAR : MANAGING SARCOPENIA AND VISCERAL FAT DRIVEN VASCULAR OUTCOMES IN PATIENTS WITH DIABETES

Dr.DEEPIKA: CLINICAL AND RADIOLOGICAL PROFILE, RISK FACTORS MANAGEMENT AND OUTCOMES IN CASES OF ACUTE STROKE

Dr. HARI PRIYA: RISK FACTORS, CLINICAL SPECTRUM, DIAGNOSTIC AND OUTCOME PREDICTORS OF PATIENTS WITH ENCEPHALOPATHY

Dr. BHARATH: OUTCOMES OF MONITORING ACUTE AND CHRONIC GLOMERULAR INJURIES WITH SPOT AND 24 HOUR URINARY PROTEIN CREATININE ESTIMATION

Dr.NISHITHA: RESOLVING DIAGNOSTIC AND THERAPEUTIC UNCERTAINTIES AND IMPROVING OUTCOMES IN PATIENTS WITH UNDIFFERENTIATED FEVER

Dr.KEERTHI: DIAGNOSTIC AND THERAPEUTIC UNCERTAINTIES IN TB

Dr.PAVANI: CLINICAL COMPLEXITIES IN MANAGMENT AND OUTCOMES OF PATIENTS WITH SIGNIFICANT ANEMIA

Dr.VENKAT SAI: ETIOLOGY AND OUTCOMES IN PATIENTS WITH SUSPECTED CLD

Dr.KRANTHI: THERAPEUTIC OUTCOMES IN PATIENTS WITH CARDIOVASCULAR DISEASES IN CKD

*Scoring systems for the research projects:

SET STANDARDS FOR COMPARISON:

DOMAIN -1: Documentation of Data Collected:

Components:

The domain "Documentation of Data Collected" assesses how well data are recorded and documented on case report forms and master charts. Here are the components of this domain:

1. Completeness of Data Entries:

   - Whether all required data fields are filled out on the case report forms and master charts is evaluated as incomplete entries can hinder the accuracy and reliability of the data.

2. Clarity and Legibility:

   - The clarity and legibility of the handwriting or documentation on the case report forms and master charts are assessed. Clear and legible documentation is essential for accurate interpretation and analysis of the data.

3. Consistency Between Forms:

   - Checking for consistency between the data recorded on the case report forms and master charts. Consistency ensures reliability and coherence in the dataset.

4. Timeliness of Data Recording:

   - Consider the timeliness of data recording on the case report forms and master charts. Timely documentation prevents delays and ensures the availability of up-to-date data for analysis.

5. Documentation of Exceptions or Deviations:

   - Assess whether any exceptions or deviations from the standard data collection procedures are documented appropriately on the case report forms and master charts. Documenting exceptions helps maintain transparency and integrity in the data.

6. Organization of Data Entries:

   - Evaluate the organization and structure of data entries on the case report forms and master charts. Well-organized data entries facilitate easy retrieval and analysis of the data.

7. Cross-Referencing and Verification:

   - Check if there are mechanisms in place for cross-referencing and verifying the data recorded on the case report forms and master charts. Cross-referencing ensures accuracy and reliability by detecting discrepancies or errors.

By assessing these components, we can determine the quality and reliability of the data documentation process in the research project.

Determination of score for domain-1 to quantify the quality: 

Poor (1):

  - Incomplete or missing data entries on case report forms and master charts.

  - Lack of clarity in handwriting or documentation, making it difficult to interpret the data.

  - Significant discrepancies between data recorded on case report forms and master charts.

Fair (2):

  - Some data entries are incomplete or missing on case report forms and master charts.

  - Handwriting or documentation may be unclear in some areas, requiring clarification.

  - Minor discrepancies between data recorded on case report forms and master charts, but most data are consistent.

Good (3):

  - All required data fields are completed on case report forms and master charts.

  - Handwriting or documentation is clear and legible, facilitating easy interpretation.

  - Consistency in data recorded on case report forms and master charts, with minimal discrepancies.

Excellent (4):

  - All data fields on case report forms and master charts are meticulously completed.

  - Handwriting or documentation is exceptionally clear and organized, enhancing data integrity.

  - Data recorded on case report forms and master charts are consistent and accurate, with no discrepancies observed.

DOMAIN-2: Clarity and Alignment of Stated Aims and Objectives:

Components:

   - Clear articulation of research aims and objectives.

   - Alignment of aims and objectives with the research question and methodology.

Determination of score for domain-2

Poor (1): The aims and objectives are unclear and not aligned with the research question.

Fair (2): The aims and objectives are somewhat clear but lack alignment with the research question.

Good (3): The aims and objectives are clearly stated and directly aligned with the research question.

Excellent (4): The aims and objectives are exceptionally clear, precise, and perfectly aligned with the research question.

DOMAIN-3: Depth and Relevance of Literature Review

Components for qualitative assessment of domain-3

   - Thoroughness in reviewing relevant literature.

   - Appropriateness of cited studies to support the rationale and context of the research.

Determination of score for domain-3 to quantify the quality

  - Poor (1): Limited literature review with minimal relevance to the research topic.

  - Fair (2): Adequate literature review with some relevant studies cited.

  - Good (3): Thorough literature review covering relevant studies and providing context for the research.

  - Excellent (4): Extensive literature review demonstrating a deep understanding of the research area and incorporating diverse perspectives.

DOMAIN-4:Methodology 

Components of the Methodology Domain:

1. Research Design:

   - The overall structure and plan of the study, including the type of research (qualitative, quantitative, mixed methods) and the rationale for choosing this design.

2. Sampling:

   - The process and criteria for selecting participants or data sources, including the sample size and sampling technique.

3. Data Collection 

   - The step-by-step process followed to collect data, ensuring consistency and reliability.

4. Ethical Considerations:

   - The measures taken to ensure ethical standards are met, including informed consent, and confidentiality.

5. Validity and Reliability:

   - The strategies used to ensure the accuracy and consistency of the data, including validation and reliability testing.

7. Data Analysis Procedures:

   - The techniques and methods used to analyze the collected data, such as thematic analysis for qualitative data or statistical analysis for quantitative data.

Determination of score for domain -4 to quantify the quality:

Poor (1): The research design, population selection, data collection methods, data collection procedures, and ethical considerations-All the components could be clearer or better described for the research question.

   - Fair (2): The research design, population selection, data collection methods, data collection procedures, and ethical considerations- less than half of the components are appropriate and clearly explained for the research question 

   - Good (3): The research design, population selection, data collection methods, data collection procedures, and ethical considerations -more than half of the components are appropriate and well-justified for the research question.

   - Excellent (4): The research design, population selection, data collection methods, data collection procedures, and ethical considerations -all the components are exceptionally clear, rigorously justified, and highly appropriate for the research question.

DOMAIN-5

FOR QUANTITIVE RESEARCH-

Quality of Tabulated Results:

Components for Qualitative Assessment of domain-5

   - Accuracy and completeness of data presented in tables.

   - Appropriateness of tables for summarizing and organizing results effectively.

Determination of score for domain-4 to quantify the quality:

Poor (1): Tables are poorly formatted or incomplete, making it difficult to interpret the data.

Fair (2): Tables are adequately formatted but may contain errors or omissions in the data presentation.

Good (3): Tables are well-organized and accurately present the data, facilitating understanding and interpretation.

Excellent (4): Tables are professionally formatted, clearly labeled, and effectively summarize the key findings of the research

FOR QUALITATIVE RESEARCH-

Quality of Data Presentation and Analysis :

Components for qualitative assessment of domain-5:

-Organization of data presentation

-Clarity and readability

-Thematic analysis: quality of thematic analysis conducted on the qualitative data (Identifying patterns, themes, and categories within the data) 

-Evaluation of depth of analysis of qualitative data

-Interpretation of findings

-Integration with research objectives

-Use of supporting evidence and references

-Coherence and consistency

-Contextualization, reflection, and reflectivity of findings

 

Determination of scoring for domain-5 in qualitative research projects to quantify the quality:

Poor (1):

  - Lack of organization or structure in presenting qualitative data.

  - Data are presented in a confusing or disorganized manner, making it difficult to understand the findings.

  - Limited or no thematic analysis or interpretation of qualitative data.

Fair (2):

   -The data presentation is somewhat organized, but improvements are needed for clarity.

  - Some attempt at thematic analysis, but it lacks depth or coherence.

  - Limited integration of qualitative data with research aims and objectives.

Good (3):

  - Data presentation is clear, coherent, and logically organized, facilitating understanding.

  - Thematic analysis identifies key themes and patterns in the qualitative data.

  - Qualitative findings are effectively integrated with research aims and objectives, enhancing the overall coherence of the research.

Excellent (4):

  - Data presentation is highly organized, engaging, and effectively communicates the qualitative findings.

  - Thematic analysis demonstrates a nuanced understanding of the data, with insightful interpretations and meaningful connections drawn between themes.

  - Qualitative findings are seamlessly integrated with research aims and objectives, contributing to a rich and comprehensive understanding of the research topic.

DOMAIN-6

Comparison with Other Studies:

Components for qualitative assessment of domain-6

   - Comprehensive comparison with findings from existing studies.

   - Identification of similarities, differences, or contradictions in results compared to other research.

Determination of score for domain-6 to quantify the quality:

Poor (1): Limited or no comparison with findings from other studies, indicating a lack of contextual understanding.

Fair (2): Some attempt to compare findings with existing literature, but it lacks depth or relevance.

Good (3): Comprehensive comparison with findings from relevant studies, highlighting similarities, differences, and implications.

Excellent (4): Exceptional comparison demonstrating a deep understanding of the literature and offering insightful insights into the research context.

DOMAIN-7

The soundness of Conclusions Drawn:

Components:

   - Logical interpretation of results concerning stated aims and objectives.

   - Justification of conclusions based on the evidence presented in the research.

Determination of score for domain-7:

Poor (1): Conclusions are unsupported by the data presented or need to logically follow from the results or no clear conclusions drawn yet.

Fair (2): Conclusions are somewhat supported by the data but lack clarity or specificity.

Good (3): Conclusions logically follow from the results and are supported by the evidence presented in the research.

Excellent (4): Conclusions are well-supported, clearly articulated, and demonstrate a nuanced understanding of the research findings.