Behavioral Perspectives on Organizational Change: Practice Adoption, Product Culling, and Technological Search

This dissertation explores the complex process of organizational change, applying a behavioral lens to understand change in processes, products, and search behaviors. Chapter 1 examines new practice adoption, exploring factors that predict the extent to which routines are adopted “as designed” within the organization. Using medical record data obtained from the hospital’s Electronic Health Record (EHR) system I develop a novel measure of the “gap” between routine “as designed” and routine “as realized.” I link this to a survey administered to the hospital’s professional staff following the adoption of a new EHR system and find that beliefs about the expected impact of the change shape fidelity of the adopted practice to its design. This relationship is more pronounced in care units with experienced professionals and less pronounced when the care unit includes departmental leadership. This research offers new insights into the determinants of routine change in organizations, in particular suggesting the beliefs held by rank-and-file members of an organization are critical in new routine adoption. Chapter 2 explores changes to products, specifically examining culling behaviors in the mobile device industry. Using a panel of quarterly mobile device sales in Germany from 2004-2009, this chapter suggests that the organization’s response to performance feedback is conditional upon the degree to which decisions are centralized. While much of the research on product exit has pointed to economic drivers or prior experience, these central finding of this chapter—that performance below aspirations decreases the rate of phase-out—suggests that firms seek local solutions when doing poorly, which is consistent with behavioral explanations of organizational action. Chapter 3 uses a novel text analysis approach to examine how the allocation of attention within organizational subunits shapes adaptation in the form of search behaviors in Motorola from 1974-1997. It develops a theory that links organizational attention to search, and the results suggest a trade-off between both attentional specialization and coupling on search scope and depth. Specifically, specialized unit attention to a more narrow set of problems increases search scope but reduces search depth; increased attentional coupling also increases search scope at the cost of depth. This novel approach and these findings help clarify extant research on the behavioral outcomes of attention allocation, which have offered mixed results.

Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients.

RATIONALE: Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections. OBJECTIVES: We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing. METHODS: The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d. MEASUREMENTS AND MAIN RESULTS: In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved. CONCLUSIONS: Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.

Fluid resuscitation practice patterns in intensive care units of the USA: a cross-sectional survey of critical care physicians.

BACKGROUND: Fluid resuscitation is a cornerstone of intensive care treatment, yet there is a lack of agreement on how various types of fluids should be used in critically ill patients with different disease states. Therefore, our goal was to investigate the practice patterns of fluid utilization for resuscitation of adult patients in intensive care units (ICUs) within the USA. METHODS: We conducted a cross-sectional online survey of 502 physicians practicing in medical and surgical ICUs. Survey questions were designed to assess clinical decision-making processes for 3 types of patients who need volume expansion: (1) not bleeding and not septic, (2) bleeding but not septic, (3) requiring resuscitation for sepsis. First-choice fluid used in fluid boluses for these 3 patient types was requested from the respondents. Descriptive statistics were performed using a Kruskal-Wallis test to evaluate differences among the physician groups. Follow-up tests, including t tests, were conducted to evaluate differences between ICU types, hospital settings, and bolus volume. RESULTS: Fluid resuscitation varied with respect to preferences for the factors to determine volume status and preferences for fluid types. The 3 most frequently preferred volume indicators were blood pressure, urine output, and central venous pressure. Regardless of the patient type, the most preferred fluid type was crystalloid, followed by 5 % albumin and then 6 % hydroxyethyl starches (HES) 450/0.70 and 6 % HES 600/0.75. Surprisingly, up to 10 % of physicians still chose HES as the first choice of fluid for resuscitation in sepsis. The clinical specialty and the practice setting of the treating physicians also influenced fluid choices. CONCLUSIONS: Practice patterns of fluid resuscitation varied in the USA, depending on patient characteristics, clinical specialties, and practice settings of the treating physicians.