Allied Systems: Data governance challenges and the opioid crisis

2021 ◽  
pp. 204388692110064
Author(s):  
Kevin McDermott
Keyword(s):  
Operations Management ◽  
Participant Observation ◽  
Business Case ◽  
Opioid Use ◽  
Data Governance ◽  
Real World Data ◽  
Trade Offs ◽  
Real People ◽  
Governance Challenges ◽  
Client Data

This teaching case based on real people and events describe a critical decision point during the nascent stage of entrepreneurial venture called Allied Systems Integration Solutions in May 2019. This data governance case explores the challenges with centralizing client data in an effort to help health practitioners cope with the opioid use crisis. Readers are asked to put themselves into the shoes of the protagonists who must make difficult operational decisions related to these data governance and behaviour management questions. This case is derived from participant observation of eight entrepreneurial mentoring sessions with the protagonist entrepreneurs. Detailed notes of participant observation sessions were maintained and qualitative data were analysed for the purpose of creating this business case. This case is intended for upper year undergraduate, or MBA courses. In particular, it should be used in curricula exploring the complexities of real-world data governance decisions, managing the trade-offs between operational efficiencies and data protection. This case would be most suited to courses in Management Information Systems, Database Management and Operations Management.

Managing Contentious Collective Action

2021 ◽  
pp. 109-129
Author(s):  
Calla Hummel
Keyword(s):  
Collective Action ◽  
Participant Observation ◽  
Street Vendor ◽  
La Paz ◽  
Street Vendors ◽  
Street Vending ◽  
Trade Offs ◽  
Survey Results ◽  
The Many ◽  
The City

Chapter 5 develops an ethnography of street vendors, their organizations, and the city officials who they interact with in the city of La Paz, Bolivia. The chapter is based on 14 months of ethnographic fieldwork in the city over four research trips in 2012, 2014 to 2015, 2018, and 2019 as well as administrative data on 31,906 street vending licenses in the city. Fieldwork included interviews, participant observation at dozens of meetings between bureaucrats and organized vendors, ride-alongs with the Municipal Guard, a street vendor survey, working as a street vendor in a clothing market, and selling wedding services with a street vendor cooperative. The theory’s observable implications are illustrated with ethnographic evidence, survey results, and license data from La Paz. I discuss how street vending has changed in the city and how officials have intervened in collective action decisions as the informal sector grew. The chapter demonstrates that officials increased benefits to organized vendors as the costs of regulating markets increased. Additionally, the leaders that take advantage of these offers tend to have more resources than their colleagues, and as the offers increased, so did the level of organization among the city’s street vendors. The chapter also discusses the many trade-offs that officials make in implementing different policies, and how officials manage the often combative organizations that they encourage.

scholarly journals The Business Case for a Journey Planning and Ticketing App—Comparison between a Simulation Analysis and Real-World Data

2020 ◽  
Vol 12 (10) ◽  
pp. 4005 ◽  
Author(s):  
Gillian Harrison ◽  
Astrid Gühnemann ◽  
Simon Shepherd
Keyword(s):  
Market Failure ◽  
Business Models ◽  
Simulation Analysis ◽  
Business Case ◽  
Transport Systems ◽  
Real World Data ◽  
Realistic Potential ◽  
Potential Adopter ◽  
Targeted Marketing ◽  
Uptake Data

Successful development of “Mobility-as-a-Service” (MaaS) schemes could be transformative to our transport systems and critical for achieving sustainable cities. There are high hopes for mobile phone applications that offer both journey planning and ticketing across all the available transport modes, but these are in their infancy, with little understanding of the correct approach to business models and governance. In this study, we develop a system dynamics diffusion model that represents the uptake of such an app, based on one developed and released in West Yorkshire, UK. We perform sensitivity and uncertainty tests on user uptake and app operating profitability, and analyse these in three key areas of marketing, competition, and costs. Comparison to early uptake data is included to demonstrate accuracy of model behaviour and would suggest market failure by month 12 without stronger marketing, even if additional tickets and functions are offered. In response to this, we offer further insights on the need for direct targeted marketing to ensure mass market adoption, the importance of understanding a realistic potential adopter pool, the awareness of competing apps, and the high uncertainty that exists in this market.

scholarly journals Efficiency and thoroughness trade-offs in high-volume organisational routines: an ethnographic study of prescribing safety in primary care

2017 ◽  
Vol 27 (3) ◽  
pp. 199-206 ◽  
Author(s):  
Suzanne Grant ◽  
Bruce Guthrie
Keyword(s):  
Primary Care ◽  
Participant Observation ◽  
High Volume ◽  
Ethnographic Study ◽  
Trade Offs ◽  
High Workload ◽  
Organisational Routines ◽  
The Face ◽  
General Practices ◽  
Context Specific

BackgroundPrescribing is a high-volume primary care routine where both speed and attention to detail are required. One approach to examining how organisations approach quality and safety in the face of high workloads is Hollnagel’s Efficiency and Thoroughness Trade-Off (ETTO). Hollnagel argues that safety is aligned with thoroughness and that a choice is required between efficiency and thoroughness as it is not usually possible to maximise both. This study aimed to ethnographically examine the efficiency and thoroughness trade-offs made by different UK general practices in the achievement of prescribing safety.MethodsNon-participant observation was conducted of prescribing routines across eight purposively sampled UK general practices. Sixty-two semistructured interviews were also conducted with key practice staff alongside the analysis of relevant practice documents.ResultsThe eight practices in this study adopted different context-specific approaches to safely handling prescription requests by variably prioritising speed of processing by receptionists (efficiency) or general practitioner (GP) clinical judgement (thoroughness). While it was not possible to maximise both at the same time, practices situated themselves at various points on an efficiency-thoroughness spectrum where one approach was prioritised at particular stages of the routine. Both approaches carried strengths and risks, with thoroughness-focused approaches considered safer but more challenging to implement in practice due to GP workload issues. Most practices adopting efficiency-focused approaches did so out of necessity as a result of their high workload due to their patient population (eg, older, socioeconomically deprived).ConclusionsHollnagel’s ETTO presents a useful way for healthcare organisations to optimise their own high-volume processes through reflection on where they currently prioritise efficiency and thoroughness, the stages that are particularly risky and improved ways of balancing competing priorities.

scholarly journals MG-RAST version 4—lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

2017 ◽  
Vol 20 (4) ◽  
pp. 1151-1159 ◽  
Author(s):  
Folker Meyer ◽  
Saurabh Bagchi ◽  
Somali Chaterji ◽  
Wolfgang Gerlach ◽  
Ananth Grama ◽  
...  
Keyword(s):  
Performance Optimization ◽  
High Performance ◽  
Lessons Learned ◽  
Added Value ◽  
Data Sets ◽  
Real World Data ◽  
Metagenome Analysis ◽  
Trade Offs ◽  
And Performance ◽  
The Right

Abstract As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.

scholarly journals A Tale of Two Linear Programming Formulations for Crashing Project Networks

2020 ◽  
Author(s):  
Collin Huse ◽  
Michael J. Brusco
Keyword(s):  
Linear Programming ◽  
Operations Management ◽  
Management Science ◽  
Alternative Formulation ◽  
Time Cost ◽  
Science Textbooks ◽  
Project Networks ◽  
Trade Offs ◽  
Decision Variables ◽  
Completion Times

Problems associated with time–cost trade-offs in project networks, which are commonly referred to as crashing problems, date back nearly 60 years. Many prominent management science textbooks provide a traditional linear programming (LP) formulation for a classic project crashing problem, in which the time–cost trade-off for each activity is continuous (and linear) over a range of possible completion times. We have found that, for students who are being introduced to time–cost trade-offs and the principles of project crashing, an alternative LP formulation facilitates a greater conceptual understanding. Moreover, the alternative formulation uses only half of the decision variables in the traditional formulation and has fewer constraints for many problems encountered in management science textbooks. Results from an MBA section of operations management suggest that students prefer the alternative formulation. Additionally, we have developed an Excel workbook that generates all possible paths for a network, allows students to manually evaluate crashing decisions, and generates the alternative LP formulation. We demonstrate the workbook using a small synthetic example and a larger, real-world network from the literature. We also show that the alternative formulation can be adapted easily to accommodate discrete project crashing problems for which the time–cost trade-offs for activities are not necessarily linear.

Identification, characterization, and chemotherapy utilization in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) using real-world data: Retrospective U.S. claims database analysis.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e17510-e17510
Author(s):  
Thomas W. Flaig ◽  
Maneesha Mehra ◽  
Robert Stellhorn ◽  
Yvette Ng ◽  
Mary Beth Todd
Keyword(s):  
Real World ◽  
Secondary Malignancy ◽  
Castration Resistant Prostate Cancer ◽  
Opioid Use ◽  
Real World Data ◽  
Claims Database ◽  
Castration Resistant ◽  
Level Data ◽  
Nationally Representative

e17510 Background: Limited data are available characterizing the progression of newly diagnosed PC to mCRPC and the use of different treatments in the mCRPC population. Real world longitudinal pt-level data were used to define, profile, and characterize treatment of pts with mCRPC. Methods: The PharMetrics database (2000-2011), a nationally representative, nonpayer–owned, integrated, commercial US claims database, was used to identify pts with an index PC diagnosis (dx) (ICD-9 codes 185.x or 233.4) and confirmation of ≥ 3 PSA test values in their claim history (n = 7402). An algorithm was developed to identify progression of PC from index dx to mCRPC by 1) evaluating the presence or absence of a secondary malignancy dx (ICD-9 codes 198.5, 199.1, and 198.82) to determine metastatic status, and 2) evaluating PSA increases with concurrent medical/surgical castration to determine castration-resistant status (Alemayehu et al, J Med Econ. 2010;13:351). Results: Mean follow-up duration was 50.7 ± 35.5 (SD) months. 271 pts (3.7%) had PC with metastases (mets) at dx. 2836 (38.3%) had medical/surgical/both castration during follow-up. 779 (11%) pts developed CRPC and 299 (4.0%) were defined as having mCRPC. Median age at PC dx and mCRPC dx was 73.5 and 76.7 years, respectively. Duration from initial PC dx to mCRPC was 28 ± 20.7 (SD) months. Prior to mCRPC dx (> 3 months), < 10% of pts were exposed to chemotherapy. Chemotherapy exposure (docetaxel, vinorelbine, estramustine, mitoxantrone) upon mCRPC dx (±3 mos) was 24%. Any docetaxel exposure after mCRPC dx was 42.8% (excluding pts with dx/follow-up pre-2004 docetaxel approval year). Other treatments in mCRPC pts at dx included opioids (n = 174, 58%) and bisphosphonates (n = 58, 19%). Conclusions: This effort identifies a methodology of using a retrospective US claims database to identify mCRPC, which accounts for less than 5% of pts with PC. 24% of pts presented with mets at initial PC dx while 76% developed mets over time. The moderate rate of opioid use and incomplete penetration of chemotherapy in mCRPC emphasizes the need for improved treatment alternatives.

Big Data Governance in Agile and Data-Driven Software Development

2019 ◽  
pp. 179-199
Author(s):  
Lili Aunimo ◽  
Ari V. Alamäki ◽  
Harri Ketamo
Keyword(s):  
Big Data ◽  
Software Development ◽  
Predictive Models ◽  
Data Privacy ◽  
Business Case ◽  
Business Environment ◽  
Data Driven ◽  
Data Governance ◽  
Governance Framework ◽  
A Company

Constructing a big data governance framework is important when a company performs data-driven software development. The most important aspects of big data governance are data privacy, security, availability, usability, and integrity. In this chapter, the authors present a business case where a framework for big data governance has been built. The business case is about the development and continuous improvement of a new mobile application that is targeted for consumers. In this context, big data is used in product development, in building predictive modes related to the users and for personalization of the product. The main finding of the study is a novel big data governance framework and that a proper framework for big data governance is useful when building and maintaining trustworthy and value adding big data-driven predictive models in an authentic business environment.

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