Intervention Effectiveness Research: Quality Improvement and Program Evaluation

von: Karen A. Monsen

Springer-Verlag, 2017

ISBN: 9783319612461

Sprache: Englisch

214 Seiten, Download: 4205 KB

Format: PDF, auch als Online-Lesen

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Intervention Effectiveness Research: Quality Improvement and Program Evaluation

	Foreword	6
	Preface	8
	Abbreviations	17
	Contents	10
	Part I: Introduction to Intervention Effectiveness Research, Quality Improvement, and Program Evaluation	18
	1: Key Concepts, Definitions, and Frameworks	19
	1.1 Introduction	19
	1.2 Definitions and Descriptions of Intervention Effectiveness Research, Quality Improvement, and Program Evaluation: What They Have in Common and How They Differ	20
	1.2.1 What Is Quality Improvement?	20
	1.2.2 What Is Program Evaluation?	21
	1.3 How Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation Are Similar	21
	1.4 How Intervention Effectiveness Research, Quality Improvement, and Outcome Evaluation Are Different	22
	1.4.1 Translational Research	22
	1.4.2 Quality Improvement (QI)	22
	1.4.3 Six Sigma Quality Improvement	23
	1.4.4 Health Services Research	23
	1.4.5 Big Data in Health Care Research	24
	1.4.6 Program Evaluation	24
	1.4.7 Implementation Research	25
	1.5 Definitions of Similar Sounding Terms and What This Book Does Not Attempt	25
	1.5.1 Comparative Effectiveness Research	25
	1.5.2 Implementation Science Research	25
	1.5.3 Dissemination Science	26
	1.6 Frameworks to Support Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation	26
	1.6.1 Theory	26
	1.6.2 Logic Models	28
	1.6.3 Theoretical Framework	28
	1.6.4 Conceptual Framework	28
	References	29
	2: Problem-Intervention-Outcome Meta-Model (PIO MM): A Conceptual Meta Model for Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation	32
	2.1 Introduction to the Problem-Intervention-Outcome Meta-Model (PIO MM)	32
	2.2 PIO MM and the CDC Logic Model	34
	2.3 PIO MM and the IHI Quality Improvement Model	35
	2.4 Using the PIO MM	37
	2.5 Operationalizing the PIO MM	40
	2.6 PIO MM Relationship to Change Theory	41
	2.7 PIO MM Relationship to PICOT	41
	References	42
	3: Problem-Intervention-Outcome Meta-Model Project Design	44
	3.1 Design for Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation	44
	3.1.1 Observational Design	44
	3.1.2 Retrospective Design	45
	3.1.3 Prospective Design	46
	3.2 Intervention and Measurement Timing	47
	3.3 PIO MM and Research Design	47
	3.4 Benefits and Challenges of the Single Group Before and After Design	47
	3.4.1 Threats to Internal Validity	48
	3.4.2 Enhancing Before and After Design Using Comparisons	48
	3.4.3 Considerations for Prospective Data Collection	49
	3.5 Comparisons Using PIO MM Variables	49
	3.5.1 Problem	49
	3.5.2 Intervention	49
	3.5.3 Interventionist	50
	3.5.4 Outcome	51
	3.5.5 Population (Individual Characteristics)	51
	3.5.6 Setting	51
	3.5.7 Time	52
	3.6 Mixed Methods: Qualitative Evaluation	52
	References	53
	4: Tools for Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation	55
	4.1 Data Sources	55
	4.2 Checklists for Obtaining New or Existing Data for Operationalizing the PIO MM	56
	4.3 Electronic Health Record Data	58
	4.4 Nursing-Specific Data	58
	4.5 Omaha System	59
	4.5.1 Problem Classification Scheme	60
	4.5.2 Intervention Scheme	60
	4.5.3 Problem Rating Scale for Outcomes	60
	4.6 Analysis Software and Techniques	62
	4.7 Power Analysis	62
	4.8 Software for Descriptive and Inferential Statistical Methods and for Creating Graphs/Charts	62
	4.8.1 Microsoft Excel	62
	4.8.2 R	63
	4.8.3 SAS	63
	4.9 Big Data (Pattern Detection) Methods	63
	4.9.1 Clustering	64
	4.9.2 Visualization	64
	4.10 Team Approach	65
	References	65
	5: Descriptive Analysis and Interpretation	67
	5.1 Introduction	67
	5.2 Data Cleaning	67
	5.2.1 Screening Phase	68
	5.2.2 Diagnostic Phase	68
	5.2.3 Treatment Phase	69
	5.2.4 Missing Data	69
	5.3 Pre-Processing	69
	5.3.1 Transforming and Recoding	69
	5.3.2 Identification and Labeling of Clusters Within a Sample	70
	5.4 Descriptive Statistics	71
	5.4.1 Frequency	72
	5.4.2 Cross Tabulation (Cross Tab) Matrix	73
	5.4.3 Rank	73
	5.4.4 Measures of Central Tendency	73
	5.4.5 Measures of Distribution	75
	References	76
	6: Inferential Analysis and Interpretation	77
	6.1 About Inferential Statistics	77
	6.2 Comparisons and Statistical Significance	80
	6.2.1 Comparisons of Sample Characteristics	80
	6.2.2 Outcomes as Measured by Before and After Comparison	80
	6.2.3 Benchmarking	82
	6.2.4 The P-Value in Large Dataset Research	83
	6.3 Clinical or Practical Significance	83
	6.3.1 Effect Size (Clinical or Practical Significance of Pchange = PTime2 ? PTime1)	83
	6.3.2 Interpretation of Effect Size (Clinical or Practical Significance)	84
	6.4 Associations	84
	6.4.1 Correlation	84
	6.4.2 Regression	86
	6.4.3 Interpretation of Correlations	86
	6.4.4 Survival Analysis (PTime1, PTime2, … PTimeX)	86
	6.4.5 Cross Tabs and Chi-Square (?2)	87
	6.5 Generalizability	87
	References	89
	7: Exploratory Data Analysis	90
	7.1 The Development of Exploratory Data Analysis	90
	7.2 Interpretation of Exploratory Data Analysis	91
	7.3 Visualization Techniques	91
	7.3.1 Heat Map	91
	7.3.2 Line Graph	93
	Line Graph with Trend Line	95
	Parallel Coordinates	95
	References	97
	8: Ethical Considerations	99
	8.1 Minimal Risk	99
	8.2 Institutional Review	100
	8.2.1 Where and How to Access an IRB	100
	8.2.2 When a Project May Be Exempt from IRB Review	100
	8.2.3 The Special Case of Quality Improvement	101
	8.2.4 Minimal Risk and IRB Review	102
	8.2.5 The Special Case of Program Evaluation	103
	8.3 Informed Consent	104
	8.3.1 What Is Informed Consent?	104
	8.3.2 Informed Consent Processes in the Context of Existing Data	105
	8.4 Data Privacy and Security	105
	References	107
	Part II: Practical Guide for Using the Problem-Intervention-Outcome Meta-Model	108
	9: Use the Worksheets and PIO MM Figure	109
	9.1 Review of Part I	109
	9.2 Overview of Part II	110
	9.2.1 Examples of Projects	110
	9.3 Starting the Process	111
	9.3.1 Worksheet Review	111
	9.3.2 Complete the PIO MM Diagram	115
	References	115
	10: Know the Literature (Worksheet A)	116
	10.1 Preparing to Complete Worksheet A	116
	10.2 Step-by Step Instructions for Completing Worksheet A	117
	10.2.1 Population of Interest	118
	10.2.2 Problem Addressed	119
	10.2.3 Measure(s) of Outcome	119
	10.2.4 Intervention(s) Used	120
	10.2.5 Measures of Intervention	121
	10.2.6 Measure of Intervention Fidelity	121
	10.2.7 Demographic Characteristics of a Sample	122
	10.2.8 Contextual Factors	122
	10.2.8.1 Contextual Factors – Interventionist	123
	10.2.9 Analysis Methods	123
	10.2.10 Comments	124
	10.2.11 Complete Reference	124
	10.3 Sources of Information for the PIO MM Matrix	124
	References	127
	11: Define the Problem (Worksheet B)	128
	11.1 Preparing to Complete Worksheet B	128
	11.2 Step-by Step Instructions for Completing Worksheet B	129
	11.2.1 Problem	129
	11.2.2 Definition of the Problem	130
	11.2.3 Population of Interest	130
	11.2.4 Background	130
	11.2.5 Problem Measurement Instrument/Scale	131
	11.2.6 Anticipated Outcome and Rationale	132
	11.2.7 What is Not Known/Gap in Knowledge	132
	References	137
	12: Describe the Intervention (Worksheet C)	139
	12.1 Preparing to Complete Worksheet C	139
	12.2 Step-by Step Instructions for Completing Worksheet C	140
	12.2.1 Describe the Intervention	140
	12.2.2 Expected Effectiveness	140
	12.2.3 Theoretical Framework	141
	12.2.4 Intervention Content and Essential Core Components	141
	12.2.5 Describe Intervention Measurement: Amount, Type, Fidelity, Quality	142
	12.2.6 Describe Interventionist Characteristics: Qualifications, Training, Demographics	143
	References	149
	13: Define the Outcome (Worksheet D)	151
	13.1 Preparing to Complete Worksheet D	151
	13.2 Step-by-Step Instructions for Completing Worksheet D	152
	References	160
	14: Plan the Analysis (Worksheet E)	162
	14.1 Preparing to Complete Worksheet E	162
	14.1.1 Step 1. Review Project Statements	163
	14.1.2 Step 2. Select Statements That Are Most Applicable to the Project and Discipline	165
	14.1.3 Step 3. Review Design Options	165
	14.1.4 Step 4. State the Design	165
	14.1.5 Step 5. Review Variables	166
	14.1.6 Step 6. Plan for Creating New Variables	166
	14.2 Step-by-Step Instructions for Completing Worksheet E	167
	14.2.1 Exploratory Data Analysis	167
	14.2.2 Sample	167
	14.2.3 Intervention	168
	14.2.4 Outcome	169
	14.2.5 Relationships Among Variables	170
	References	171
	15: Interpret the Results (Worksheet F)	173
	15.1 Preparing to Complete Worksheet F	173
	15.2 Results Statements and Presentation	174
	15.2.1 Presenting the Results	174
	15.2.2 Description of Sample Characteristics	174
	15.2.3 Description of Interventions	176
	15.2.4 Description of Outcomes	177
	15.2.5 Description of Benchmark Attainment	179
	15.2.6 Correlations Between Interventions and Outcomes	180
	15.3 Results Interpretation	181
	15.3.1 Theoretical Framework-Related Interpretation	182
	15.3.2 Temporality-Related Interpretation	182
	15.3.3 Give Alternative Explanations for the Findings	183
	References	184
	16: Disseminate the Findings	185
	16.1 Why Dissemination Matters	185
	16.2 Getting the Most Benefit from This Chapter	186
	16.3 Iterative Interpretation and Explication of the Overall Story	186
	16.4 Drafting the Abstract: Summarize the Story in Brief	190
	16.5 Develop and Display Results	191
	16.6 Adding Meaningful Interpretation to the Results	193
	16.7 Limitations	199
	16.8 The Methods Section	200
	16.9 The Purpose Statement	201
	16.10 Background to Set the Stage for the Purpose	201
	16.11 The Gap in Knowledge	203
	16.12 Title, Abstract, and Conclusion	204
	16.13 Rewrite the Abstract	205
	16.14 Write the Conclusions Section	205
	16.15 Polishing Tips	207
	16.16 Styles and Author Guidelines	207
	References	208
	17: Synthesis, Next Steps, and Epilogue	210
	17.1 Planning Next Steps	210
	17.2 Questions to Inspire Next Steps	212
	17.3 Building Evidence on Evidence	213
	References	214

Intervention Effectiveness Research: Quality Improvement and Program Evaluation

von: Karen A. Monsen

Intervention Effectiveness Research: Quality Improvement and Program Evaluation

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