Essays on Healthcare Operations Restricted; Files Only

Abstract

The underlying complexity of healthcare systems creates challenges in delivering patient care. Operations management is uniquely equipped to analyze process efficiency in healthcare organizations, and improve access and quality of care while optimizing overall costs. This work tackles three important questions to healthcare practitioners, while contributing to the operations management literature.

The first essay studies a multi-channel healthcare system where physicians can diagnose patients and prescribe treatment remotely or in-person. Results show that the introduction of a telemedicine channel leads to higher patient complexity in the in-person channel. In addition, average waiting time reduces for in-person appointments (by 37.5%), and for medically necessary procedures (by 43%), despite the increase in the total number of patients seen at the specialist clinic.

 The second essay makes the business case for investing in nursing by quantifying the economic value created by nursing productivity. Results show that lightening a nurse’s workload by one patient is associated with a 14% service speedup for every patient under the nurse’s care in the emergency department. A counterfactual analysis suggests that adding one more nurse per shift can generate significant savings for society and for the emergency department.

 The third essay evaluates the accuracy of skin cancer diagnoses in administrative healthcare data. The analysis of administrative healthcare data, which often suffer from coding errors, is improved by the construction and validation of case-identifying algorithms that reduce misclassification errors. The proposed algorithms can increase the rate of correct identification of skin cancers up to 96% on patient level.

Table of Contents

Chapter 1: Multi-channel Healthcare — Impact of Telemedicine Adoption on Patient Flow . . .1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.1 Queues in Healthcare Operations . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.2 Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2.3 Telemedicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Research Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Hypotheses Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4.1 Patient Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4.2 System Throughput and Timely Access to Care . . . . . . . . . . . . . . . 12

1.5 Data and Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.5.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.5.2 Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.6.1 Patient Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.6.2 Throughput Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.6.3 Timely Access to Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.7 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.7.1 Parallel Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.7.2 Placebo Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Chapter 2: We Should Invest in Nurses — Valuing Nursing Productivity in Emergency Departments . . .32

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.2.1 The Effect of Nurseload in Healthcare . . . . . . . . . . . . . . . . . . . . 35

2.2.2 Workload and Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.4.1 Model-free Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.4.2 Parametric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.5 Counterfactual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.5.1 Social Welfare Gains from Reduced Length of Stays . . . . . . . . . . . . 45

2.5.2 Financial Impact on ED from Reduced Service Times . . . . . . . . . . . . 46

2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Chapter 3: Accuracy of Administrative Data in Healthcare—a Case Study of Skin Cancers . . .49

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2 Study Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.2.1 Administrative Data Collection . . . . . . . . . . . . . . . . . . . . . . . 51

3.2.2 Manual Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.2.3 Case-identifying Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

About this Dissertation

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School	Laney Graduate School
Department	Business
Subfield / Discipline	Information Systems & Operations Management
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Health Sciences, Health Care Management Operations Research
Palabra Clave	nursing case-identifying algorithms healthcare operations telemedicine
Committee Chair / Thesis Advisor	KC, Diwas Singh, Emory University
Committee Members	Yeung, Howa, Emory University Osadchiy, Nikolay, Emory University Lee, Donald, Emory University

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