Some 70,000 people in New Zealand may have undiagnosed diabetes. This study aims to develop ‘systematic opportunistic screening’ for diabetes, testing people attending a general practitioner (GP) for some other reason, and to trial this process with Auckland GPs. The literature on how to change doctor behaviour is reviewed for both theoretical perspectives and empirical evidence. Two of the most promising strategies are computer reminders within a medical consultation and having patients influence doctors. Literature reviews cover GP attitudes to diabetes, guidelines and preventive care and the role of a computer in a GP consultation. The Mail Survey (response rate 154/212, 72.6%) reports GP attitudes to guidelines and preventive care. Factor analysis showed five ‘guidelines’ factors and two ‘preventive care’ factors that might indicate differential motivations to screening for diabetes. The Focus Group Study, of 35 GPs in 5 groups, discussed guidelines, diabetes and computer reminders in a consultation. The analysis suggested that GPs would respond to a patient reminder and may respond to a computer reminder to screen for diabetes. The Screening Reminder Trial involved 107 GPs randomly allocated across four interventions: Computer reminders, Patient reminders, Both and Usual care. The main outcome measures were whether a patient who was eligible for diabetes screening and who visited a GP during the trial had a glucose test done within the trial. The trial ran for two months. Analysis was by intention-to-treat and allowed for clustering by GP. Compared with the Usual care group (screening rate 15.5%), the Odds Ratio of eligible patients being screened were; Computer group OR 2.55 (1.68-3.88), Patient group OR 1.72 (1.21-2.43) and Both group OR 1.69 (1.11-2.59). The Computer reminders were more acceptable to GPs than were the Patient intervention. The findings suggest that a simple computer reminder can implement systematic opportunistic screening for diabetes in New Zealand. If all GPs in New Zealand used the computer reminders for one year, some 8000 patients might benefit from having their diabetes treated for five years longer than they would have under ‘usual care’. / Subscription resource available via Digital Dissertations only.

Thesis (Ph.D.)--Pacific Graduate School of Psychology, 1997. / Source: Dissertation Abstracts International, Volume: 59-07, Section: B, page: 3713. Adviser: E. F. Duran.

Thesis (Ph.D.)--Pacific Graduate School of Psychology, 1999. / Source: Dissertation Abstracts International, Volume: 60-06, Section: B, page: 2958. Adviser: Bruce Bongar.

Thesis (Ph.D.)--Arizona State University, 2005. / (UnM)AAI3194930. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 5749. Adviser: William (Dennis) Clark.

Thesis (Ph.D.)--University of California, San Francisco, 2005. / Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6526. Adviser: Kathleen Dracup.

Thesis (Ph.D.)--University of California, San Francisco, 2007. / Source: Dissertation Abstracts International, Volume: 68-04, Section: A, page: 1684. Adviser: Robert Newcomer.

Thesis (Ph.D.)--University of California, San Francisco, 2008. / Source: Dissertation Abstracts International, Volume: 69-09, Section: A, page: 3767. Adviser: Carroll L. Estes.

Thesis (Ph.D.)--University of California, San Francisco, 2009. / Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: . Adviser: Kathleen A. Dracup.

Some 70,000 people in New Zealand may have undiagnosed diabetes. This study aims to develop ‘systematic opportunistic screening’ for diabetes, testing people attending a general practitioner (GP) for some other reason, and to trial this process with Auckland GPs. The literature on how to change doctor behaviour is reviewed for both theoretical perspectives and empirical evidence. Two of the most promising strategies are computer reminders within a medical consultation and having patients influence doctors. Literature reviews cover GP attitudes to diabetes, guidelines and preventive care and the role of a computer in a GP consultation. The Mail Survey (response rate 154/212, 72.6%) reports GP attitudes to guidelines and preventive care. Factor analysis showed five ‘guidelines’ factors and two ‘preventive care’ factors that might indicate differential motivations to screening for diabetes. The Focus Group Study, of 35 GPs in 5 groups, discussed guidelines, diabetes and computer reminders in a consultation. The analysis suggested that GPs would respond to a patient reminder and may respond to a computer reminder to screen for diabetes. The Screening Reminder Trial involved 107 GPs randomly allocated across four interventions: Computer reminders, Patient reminders, Both and Usual care. The main outcome measures were whether a patient who was eligible for diabetes screening and who visited a GP during the trial had a glucose test done within the trial. The trial ran for two months. Analysis was by intention-to-treat and allowed for clustering by GP. Compared with the Usual care group (screening rate 15.5%), the Odds Ratio of eligible patients being screened were; Computer group OR 2.55 (1.68-3.88), Patient group OR 1.72 (1.21-2.43) and Both group OR 1.69 (1.11-2.59). The Computer reminders were more acceptable to GPs than were the Patient intervention. The findings suggest that a simple computer reminder can implement systematic opportunistic screening for diabetes in New Zealand. If all GPs in New Zealand used the computer reminders for one year, some 8000 patients might benefit from having their diabetes treated for five years longer than they would have under ‘usual care’. / Subscription resource available via Digital Dissertations only.

abstract: Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model is used to evaluate how scheduling heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared to current practice. Next, a bi-criteria Genetic Algorithm is used to determine if better solutions can be obtained for this single day scheduling problem. The efficacy of the bi-criteria Genetic Algorithm, when surgeries are allowed to be moved to other days, is investigated. Numerical experiments based on real data from a large health care provider are presented. The analysis provides insight into the best scheduling heuristics, and the tradeoff between patient and health care provider based criteria. Second, a multi-stage stochastic mixed integer programming formulation for the allocation of surgeries to ORs over a finite planning horizon is studied. The demand for surgery and surgical duration are random variables. The objective is to minimize two competing criteria: expected surgery cancellations and OR overtime. A decomposition method, Progressive Hedging, is implemented to find near optimal surgery plans. Finally, properties of the model are discussed and methods are proposed to improve the performance of the algorithm based on the special structure of the model. It is found simple rules can improve schedules used in practice. Sequencing surgeries from the longest to shortest mean duration causes high expected overtime, and should be avoided, while sequencing from the shortest to longest mean duration performed quite well in our experiments. Expending greater computational effort with more sophisticated optimization methods does not lead to substantial improvements. However, controlling daily procedure mix may achieve substantial improvements in performance. A novel stochastic programming model for a dynamic surgery planning problem is proposed in the dissertation. The efficacy of the progressive hedging algorithm is investigated. It is found there is a significant correlation between the performance of the algorithm and type and number of scenario bundles in a problem instance. The computational time spent to solve scenario subproblems is among the most significant factors that impact the performance of the algorithm. The quality of the solutions can be improved by detecting and preventing cyclical behaviors. / Dissertation/Thesis / Ph.D. Industrial Engineering 2010