Ronald J Ozminkowski - Academia.edu (original) (raw)
Papers by Ronald J Ozminkowski
Open Data Science, 2023
This paper is Part 2 of a three-part series. Part 1 introduced readers to ChatGPT and how it can... more This paper is Part 2 of a three-part series. Part 1 introduced readers to ChatGPT and how it can be used to learn more about a topic (specifically, data centers) that has many different technical, business, environmental, political, and economic considerations. The major focus of Part 1 was describing what ChatGPT is, how it came to be, how it works, and how it can be used to address technical question about data centers. Those issues included descriptions of the types of data centers, the infrastructure required to create these centers, and alternatives to using them, such as edge computing and cloud computing. The utility of data centers for high performance and quantum computing was also described at a high level.
Here in Part 2, we move to business considerations that can help data scientists, managers, and executives weigh the utility of investing in data centers. We focus on staffing, budgeting, and financial issues. By using ChatGPT, we can gain a great deal of knowledge about data centers that would otherwise take much longer and cost much more to obtain. Still, we keep in mind the need for humans to guide the ChatGPT conversation to prevent drifts or ‘hallucinations’ that might otherwise lead the conversation astray.
Open Data Science, 2023
This is part 1 of a three-part series about how to use ChatGPT to find information about topics t... more This is part 1 of a three-part series about how to use ChatGPT to find information about topics that have many technical, business, economic, environmental, and political considerations. The case in point is data centers.
To learn more about data centers I began by asking ChatGPT what Chief Transformation Officers should know about them. This interaction is described in my upcoming article in CXOTech Magazine. Next, I asked what a data center typically looks like and how it should be staffed. Then I asked how much of a company’s budget is typically devoted to data center use, compared to budgets for many other parts of the business. Structural issues are covered below, while staffing, budgeting, and other business issues are covered in Part 2 of this series.
Alternatives to data centers such as cloud storage and hybrid solutions exist, so I asked ChatGPT to compare those as well, and its responses are below. Then I asked about the build or buy options to finance data centers or alternatives; this is covered in Part 2 as well.
Since a key function of a data center is to house data and help users prepare data for complex analytics, I also asked ChatGPT to comment on the ability to conduct high-performance computing with versus without a data center. Its response is below.
Finally, I asked ChatGPT about security concerns, some compelling political and economic controversies around them, and how data centers are likely to evolve in the next 5-10 years. These are addressed in Part 3 of this series.
Environmental, political, and economic concerns can easily derail or complicate decisions about h... more Environmental, political, and economic concerns can easily derail or complicate decisions about how to invest in or use data center technology. In this article, I report my interactions with ChatGPT to learn more about these. Some of the environmental and political issues described are also related to security, so we start the interaction there. Security and environmental concerns will influence how data centers are likely to evolve over the next decade, as ChatGPT points out below too.
Next, like every technology, there is a chance that data centers will become obsolete at some point, so I asked ChatGPT to speculate about how they may evolve.
Several political, economic, and environmental issues about data centers are controversial. Data scientists and business leaders should consider these before investing. ChatGPT found some of these controversies, and I expand on that later in the Discussion section.
While questions about these topics complete my three-part series with ChatGPT about data centers, keep in mind that recommendations for how to use ChatGPT and other large language model (LLM)-based chatbots continue to evolve. I therefore add some guidance that users can adopt to make sure they get the most out of their interactions with these chatbots.
American Journal of Health Promotion, 2010
Oncology Research and Treatment, 1994
Medical Care, 1992
This study examines whether patient outcomes are affected by changes in volume over time within h... more This study examines whether patient outcomes are affected by changes in volume over time within hospitals and whether such effects are consistent with cross-sectional results previously reported in the literature. Investigating the existence of volume-outcome relationships longitudinally for specific groups of patients relates directly to the policy issue of whether, and how, specific inpatient services should be regionalized. The analysis uses up to 8 years of observations from a national sample of nearly 500 community hospitals. Outcomes are measured as inhospital mortality adjusted for case severity. Instrumental variables techniques are used to test and control for the possibility of selective referral. The results suggest that higher volume leads to better outcomes for certain groups of patients. Among the groups studied here, increases in volume lowered adjusted mortality rates for acute myocardial infarction, hernia repair, and respiratory distress syndrome in neonates; corre...
Managed care quarterly, 1994
An insurance claims databased profiling system was developed to help select new primary care phys... more An insurance claims databased profiling system was developed to help select new primary care physicians (PCPs) for a managed care network. PCPs (family practitioners, internists, and pediatricians) were ranked based on how closely their actual use of outpatient services conformed to the predictions of a mathematical model that adjusted for differences in age, sex, and case mix.
Medical Care, 1993
Because of a shortage of usable organs, many who require heart or liver transplants for survival ... more Because of a shortage of usable organs, many who require heart or liver transplants for survival will not have access to them. Access to care may reflect demographic factors and ability to pay, as well as medical considerations. Receipt of an organ may be influenced by expected survival with and without a transplant, age, gender, race, ability to pay, and distance to a transplant center. Discharge abstract data from a national sample of over 500 hospitals in 1986 and 1987 were used to select heart and liver recipients and others with end-stage diseases who did not receive a transplant. Multivariate logistic regression analyses were then used to estimate how receipt of a transplant was influenced by expected years of survival after transplantation (YAT), expected ability to pay, age, sex, race, and distance to a transplant center. Controlling for differences in expected YAT, age, sex, race, and distance to the transplant center, those expected to have the most ability to pay were mor...
Developments in health economics and public policy, 1992
Excess demand and patient selection for heart and liver transplantation* BERNARD FRIEDMAN, RONALD... more Excess demand and patient selection for heart and liver transplantation* BERNARD FRIEDMAN, RONALD J. OZMINKOWSKI and ZACHARY TAYLOR Division of Provider Studies, Agency for Health Care Policy and Research, 5600 Fishers Lane, Rockville, MD ...
Health services research, 1998
OBJECTIVE To study the effectiveness of a 1990 ban by New York state on entry to more than one wa... more OBJECTIVE To study the effectiveness of a 1990 ban by New York state on entry to more than one waiting list for a cadaver kidney transplant, and the impact of the ban on equity in access to transplantation. DATA SOURCES (1) Waiting list files from the Organ Procurement and Transplantation Network, (2) the Health Care Financing Administration's Medicare Program Management and Medical Information System, and (3) U.S. Census Public Use Files. STUDY DESIGN Multivariate hazard models were used to estimate the impact of the ban of the overall odds of multiple listing and on the odds of multiple listing at in-state and out-of-state transplant centers. After estimating the relationship between multiple listing and subsequent transplantation, we used simulation techniques to estimate the effects of a complete multiple listing ban on group waiting time differentials. Independent variables included demographic/socioeconomic characteristics, measures of ESRD severity, general transplantatio...
PURPOSE To examine the relationship between hemoglobin A1c (HbA1c) test rates and values and vari... more PURPOSE To examine the relationship between hemoglobin A1c (HbA1c) test rates and values and various self-reported measures of health status within a sample of diabetes patients drawn from 11 California health plans, with a focus on improving diabetes care in this patient population. DESIGN The analysis relies on data obtained from medical records of a sample population of 4,747 diabetes patients and a patient survey mailed to a large subsample of patients included in the medical-records analysis. METHODS Descriptive methods were used to compare the medical records and survey-data results. PRINCIPAL FINDINGS There were substantive differences noted between diabetes patients' self-reported health status, their level of satisfaction with the care they received, and the actual care they received. There was a large discrepancy between diabetes patients' perceptions of the care they received for their diabetes, which was overwhelmingly positive, and the HbA1c test-frequency rates...
Organ Allocation, 1998
In the United States, persons awaiting cadaveric kidneys for transplantation are listed by a tran... more In the United States, persons awaiting cadaveric kidneys for transplantation are listed by a transplant center with an Organ Procurement Organization (OPO) that belongs to the national Organ Procurement and Transplantation Network (OPTN). The OPTN is operated under federal contract by the United Network for Organ Sharing and maintains a national list of all patients awaiting cadaveric kidneys for transplantation. Most kidney transplant candidates are on the waiting list at a transplant center in their local area. In most cases, transplant centers use the local OPO or, if there is more than one OPO in an area, transplant centers may choose the OPO to list their transplant patients. According to a policy statement released in March 1995, current OPTN rules prohibit a transplant center from listing the same patient with more than one OPO. Patients, however, are free to pursue waiting lists in multiple OPOs.
Business and health, 2000
Health care financing review, 1997
A telephone survey of a national sample of 515 Medicare End Stage Renal Disease Program beneficia... more A telephone survey of a national sample of 515 Medicare End Stage Renal Disease Program beneficiaries was conducted to obtain information on their health status and its determinants. The Medical Outcomes Study Short Form-36 (SF-36) was applied during the interview process to obtain the health-status information. The reliability of each SF-36 health-status dimension was at least 0.85, and the validity of seven of the eight dimensions was high. Weighted least-squares regression results showed that health-status levels were often lower among older patients and Hispanic persons, and sometimes lower for those with low incomes. The implications of using the SF-36 for health-status measurement are also described.
Open Data Science, 2023
This paper is Part 2 of a three-part series. Part 1 introduced readers to ChatGPT and how it can... more This paper is Part 2 of a three-part series. Part 1 introduced readers to ChatGPT and how it can be used to learn more about a topic (specifically, data centers) that has many different technical, business, environmental, political, and economic considerations. The major focus of Part 1 was describing what ChatGPT is, how it came to be, how it works, and how it can be used to address technical question about data centers. Those issues included descriptions of the types of data centers, the infrastructure required to create these centers, and alternatives to using them, such as edge computing and cloud computing. The utility of data centers for high performance and quantum computing was also described at a high level.
Here in Part 2, we move to business considerations that can help data scientists, managers, and executives weigh the utility of investing in data centers. We focus on staffing, budgeting, and financial issues. By using ChatGPT, we can gain a great deal of knowledge about data centers that would otherwise take much longer and cost much more to obtain. Still, we keep in mind the need for humans to guide the ChatGPT conversation to prevent drifts or ‘hallucinations’ that might otherwise lead the conversation astray.
Open Data Science, 2023
This is part 1 of a three-part series about how to use ChatGPT to find information about topics t... more This is part 1 of a three-part series about how to use ChatGPT to find information about topics that have many technical, business, economic, environmental, and political considerations. The case in point is data centers.
To learn more about data centers I began by asking ChatGPT what Chief Transformation Officers should know about them. This interaction is described in my upcoming article in CXOTech Magazine. Next, I asked what a data center typically looks like and how it should be staffed. Then I asked how much of a company’s budget is typically devoted to data center use, compared to budgets for many other parts of the business. Structural issues are covered below, while staffing, budgeting, and other business issues are covered in Part 2 of this series.
Alternatives to data centers such as cloud storage and hybrid solutions exist, so I asked ChatGPT to compare those as well, and its responses are below. Then I asked about the build or buy options to finance data centers or alternatives; this is covered in Part 2 as well.
Since a key function of a data center is to house data and help users prepare data for complex analytics, I also asked ChatGPT to comment on the ability to conduct high-performance computing with versus without a data center. Its response is below.
Finally, I asked ChatGPT about security concerns, some compelling political and economic controversies around them, and how data centers are likely to evolve in the next 5-10 years. These are addressed in Part 3 of this series.
Environmental, political, and economic concerns can easily derail or complicate decisions about h... more Environmental, political, and economic concerns can easily derail or complicate decisions about how to invest in or use data center technology. In this article, I report my interactions with ChatGPT to learn more about these. Some of the environmental and political issues described are also related to security, so we start the interaction there. Security and environmental concerns will influence how data centers are likely to evolve over the next decade, as ChatGPT points out below too.
Next, like every technology, there is a chance that data centers will become obsolete at some point, so I asked ChatGPT to speculate about how they may evolve.
Several political, economic, and environmental issues about data centers are controversial. Data scientists and business leaders should consider these before investing. ChatGPT found some of these controversies, and I expand on that later in the Discussion section.
While questions about these topics complete my three-part series with ChatGPT about data centers, keep in mind that recommendations for how to use ChatGPT and other large language model (LLM)-based chatbots continue to evolve. I therefore add some guidance that users can adopt to make sure they get the most out of their interactions with these chatbots.
American Journal of Health Promotion, 2010
Oncology Research and Treatment, 1994
Medical Care, 1992
This study examines whether patient outcomes are affected by changes in volume over time within h... more This study examines whether patient outcomes are affected by changes in volume over time within hospitals and whether such effects are consistent with cross-sectional results previously reported in the literature. Investigating the existence of volume-outcome relationships longitudinally for specific groups of patients relates directly to the policy issue of whether, and how, specific inpatient services should be regionalized. The analysis uses up to 8 years of observations from a national sample of nearly 500 community hospitals. Outcomes are measured as inhospital mortality adjusted for case severity. Instrumental variables techniques are used to test and control for the possibility of selective referral. The results suggest that higher volume leads to better outcomes for certain groups of patients. Among the groups studied here, increases in volume lowered adjusted mortality rates for acute myocardial infarction, hernia repair, and respiratory distress syndrome in neonates; corre...
Managed care quarterly, 1994
An insurance claims databased profiling system was developed to help select new primary care phys... more An insurance claims databased profiling system was developed to help select new primary care physicians (PCPs) for a managed care network. PCPs (family practitioners, internists, and pediatricians) were ranked based on how closely their actual use of outpatient services conformed to the predictions of a mathematical model that adjusted for differences in age, sex, and case mix.
Medical Care, 1993
Because of a shortage of usable organs, many who require heart or liver transplants for survival ... more Because of a shortage of usable organs, many who require heart or liver transplants for survival will not have access to them. Access to care may reflect demographic factors and ability to pay, as well as medical considerations. Receipt of an organ may be influenced by expected survival with and without a transplant, age, gender, race, ability to pay, and distance to a transplant center. Discharge abstract data from a national sample of over 500 hospitals in 1986 and 1987 were used to select heart and liver recipients and others with end-stage diseases who did not receive a transplant. Multivariate logistic regression analyses were then used to estimate how receipt of a transplant was influenced by expected years of survival after transplantation (YAT), expected ability to pay, age, sex, race, and distance to a transplant center. Controlling for differences in expected YAT, age, sex, race, and distance to the transplant center, those expected to have the most ability to pay were mor...
Developments in health economics and public policy, 1992
Excess demand and patient selection for heart and liver transplantation* BERNARD FRIEDMAN, RONALD... more Excess demand and patient selection for heart and liver transplantation* BERNARD FRIEDMAN, RONALD J. OZMINKOWSKI and ZACHARY TAYLOR Division of Provider Studies, Agency for Health Care Policy and Research, 5600 Fishers Lane, Rockville, MD ...
Health services research, 1998
OBJECTIVE To study the effectiveness of a 1990 ban by New York state on entry to more than one wa... more OBJECTIVE To study the effectiveness of a 1990 ban by New York state on entry to more than one waiting list for a cadaver kidney transplant, and the impact of the ban on equity in access to transplantation. DATA SOURCES (1) Waiting list files from the Organ Procurement and Transplantation Network, (2) the Health Care Financing Administration's Medicare Program Management and Medical Information System, and (3) U.S. Census Public Use Files. STUDY DESIGN Multivariate hazard models were used to estimate the impact of the ban of the overall odds of multiple listing and on the odds of multiple listing at in-state and out-of-state transplant centers. After estimating the relationship between multiple listing and subsequent transplantation, we used simulation techniques to estimate the effects of a complete multiple listing ban on group waiting time differentials. Independent variables included demographic/socioeconomic characteristics, measures of ESRD severity, general transplantatio...
PURPOSE To examine the relationship between hemoglobin A1c (HbA1c) test rates and values and vari... more PURPOSE To examine the relationship between hemoglobin A1c (HbA1c) test rates and values and various self-reported measures of health status within a sample of diabetes patients drawn from 11 California health plans, with a focus on improving diabetes care in this patient population. DESIGN The analysis relies on data obtained from medical records of a sample population of 4,747 diabetes patients and a patient survey mailed to a large subsample of patients included in the medical-records analysis. METHODS Descriptive methods were used to compare the medical records and survey-data results. PRINCIPAL FINDINGS There were substantive differences noted between diabetes patients' self-reported health status, their level of satisfaction with the care they received, and the actual care they received. There was a large discrepancy between diabetes patients' perceptions of the care they received for their diabetes, which was overwhelmingly positive, and the HbA1c test-frequency rates...
Organ Allocation, 1998
In the United States, persons awaiting cadaveric kidneys for transplantation are listed by a tran... more In the United States, persons awaiting cadaveric kidneys for transplantation are listed by a transplant center with an Organ Procurement Organization (OPO) that belongs to the national Organ Procurement and Transplantation Network (OPTN). The OPTN is operated under federal contract by the United Network for Organ Sharing and maintains a national list of all patients awaiting cadaveric kidneys for transplantation. Most kidney transplant candidates are on the waiting list at a transplant center in their local area. In most cases, transplant centers use the local OPO or, if there is more than one OPO in an area, transplant centers may choose the OPO to list their transplant patients. According to a policy statement released in March 1995, current OPTN rules prohibit a transplant center from listing the same patient with more than one OPO. Patients, however, are free to pursue waiting lists in multiple OPOs.
Business and health, 2000
Health care financing review, 1997
A telephone survey of a national sample of 515 Medicare End Stage Renal Disease Program beneficia... more A telephone survey of a national sample of 515 Medicare End Stage Renal Disease Program beneficiaries was conducted to obtain information on their health status and its determinants. The Medical Outcomes Study Short Form-36 (SF-36) was applied during the interview process to obtain the health-status information. The reliability of each SF-36 health-status dimension was at least 0.85, and the validity of seven of the eight dimensions was high. Weighted least-squares regression results showed that health-status levels were often lower among older patients and Hispanic persons, and sometimes lower for those with low incomes. The implications of using the SF-36 for health-status measurement are also described.