The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support

Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model

BACKGROUND: Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients. METHODOLOGY AND PRINCIPAL FINDINGS: This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable. SIGNIFICANCE: Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients

Is U.S. health care an appropriate system? A strategic perspective from systems science

<p>Abstract</p> <p>Context</p> <p>Systems science provides organizational principles supported by biologic findings that can be applied to any organization; any incongruence indicates an incomplete or an already failing system. U.S. health care is commonly referred to as a system that consumes an ever- increasing percentage of the gross domestic product and delivers seemingly diminishing value.</p> <p>Objective</p> <p>To perform a comparative study of U.S. health care with the principles of systems science and, if feasible, propose solutions.</p> <p>Design</p> <p>General systems theory provides the theoretical foundation for this observational research.</p> <p>Main Outcome Measures</p> <p>A degree of compliance of U.S. health care with systems principles and its space-time functional location within the dynamic systems model.</p> <p>Results of comparative analysis</p> <p>U.S. health care is an incomplete system further threatened by the fact that it functions in the zone of chaos within the dynamic systems model.</p> <p>Conclusion</p> <p>Complying with systems science principles and the congruence of pertinent cycles, U.S. health care would likely dramatically improve its value creation for all of society as well as its resiliency and long-term sustainability.</p> <p>Immediate corrective steps could be taken: Prioritize and incentivize <it>health </it>over <it>care</it>; restore fiscal soundness by combining health and life insurance for the benefit of the insured and the payer; rebalance horizontal/providers and vertical/government hierarchies.</p

A new model for health care delivery

<p>Abstract</p> <p>Background</p> <p>The health care delivery system in the United States is facing cost and quality pressures that will require fundamental changes to remain viable. The optimal structures of the relationships between the hospital, medical school, and physicians have not been determined but are likely to have a large impact on the future of healthcare delivery. Because it is generally agreed that academic medical centers will play a role in the sustainability of this future system, a fundamental understanding of the relative contributions of the stakeholders is important as well as creativity in developing novel strategies to achieve a shared vision.</p> <p>Discussion</p> <p>Core competencies of each of the stakeholders (the hospital, the medical school and the physicians) must complement the others and should act synergistically. At the same time, the stakeholders should determine the common core values and should be able to make a meaningful contribution to the delivery of health care.</p> <p>Summary</p> <p>Health care needs to achieve higher quality and lower cost. Therefore, in order for physicians, medical schools, and hospitals to serve the needs of society in a gratifying way, there will need to be change. There needs to be more scientific and social advances. It is obvious that there is a real and urgent need for relationship building among the professionals whose duty it is to provide these services.</p

Cost-analysis of XELOX and FOLFOX4 for treatment of colorectal cancer to assist decision-making on reimbursement

<p>Abstract</p> <p>Background</p> <p>XELOX (capecitabine + oxaliplatin) and FOLFOX 4 (5-FU + folinic acid + oxaliplatin) have shown similar improvements in survival in patients with metastatic colorectal cancer (MCRC). A US cost-minimization study found that the two regimens had similar costs from a healthcare provider perspective but XELOX had lower costs than FOLFOX4 from a societal perspective, while a Japanese cost-effectiveness study found XELOX had superior cost-effectiveness. This study compared the costs of XELOX and FOLFOX4 in patients with MCRC recently treated in two oncology departments in Hong Kong.</p> <p>Methods</p> <p>Cost data were collected from the medical records of 60 consecutive patients (30 received XELOX and 30 FOLFOX4) from two hospitals. Drug costs, outpatient visits, hospital days and investigations were recorded and expressed as cost per patient from the healthcare provider perspective. Estimated travel and time costs were included in a societal perspective analysis. All costs were classed as either scheduled (associated with planned chemotherapy and follow-up) or unscheduled (unplanned visits or admissions and associated tests and medicines). Costs were based on government and hospital sources and expressed in US dollars (US$).</p> <p>Results</p> <p>XELOX patients received an average of 7.3 chemotherapy cycles (of the 8 planned cycles) and FOLFOX4 patients received 9.2 cycles (of the 12 planned cycles). The scheduled cost per patient per cycle was$2,046 for XELOX and $2,152 for FOLFOX4, while the unscheduled cost was$240 and $421, respectively. Total treatment cost per patient was$16,609 for XELOX and $23,672 for FOLFOX4; the total cost for FOLFOX4 was 37$17,836 for XELOX and $27,455 for FOLFOX4. Sensitivity analyses showed XELOX was still less costly than FOLFOX4 when using full drug regimen costs, incorporating data from a US model with costs and adverse event data from their clinical trial and with the removal of oxaliplatin from both treatment arms. Capecitabine would have to cost around four times its present price in Hong Kong for the total resource cost of treatment with XELOX to equal that of FOLFOX4.</p> <p>Conclusion</p> <p>XELOX costs less than FOLFOX4 for this patient group with MCRC from both the healthcare provider and societal perspectives.</p

Determinants of out-of-pocket pharmaceutical expenditure and access to drugs in Catalonia

This article examines the determinants of the demand for pharmaceuticals in Catalonia using the latest available official survey data, having accounted for the individual differences in cost-sharing among consumers, as well as the influence of self-medication. The article builds a model of the (household) determinants of pharmaceutical expenditure and consumption. The econometric application deals with the infrequency of purchases and the nondistinction between out-of-pocket spending and drug cost sharing. Our results suggest that income and the variables accounting for the effect of cost sharing were significantly associated with drug use but not drug expenditure. Furthermore, gender, health status and having health insurance appear to be significant predictors. Access to pharmacies increases both drug use and expenditure; self-medication also increases drug expenditure out-of-pocket. These results are relevant for the entire Spanish case where current reform proposals have recommended the introduction of income-related (rather than age-related) co-payments. The same applies to the need for controlling self-medication which results from individual behaviour of consuming medicines for minor ailments without a prescription and which can be acquired OTC and are priced cheaply.

Use of Radcube for Extraction of Finding Trends in a Large Radiology Practice

The purpose of our study was to demonstrate the use of Natural Language Processing (Leximer), along with Online Analytic Processing, (NLP-OLAP), for extraction of finding trends in a large radiology practice. Prior studies have validated the Natural Language Processing (NLP) program, Leximer for classifying unstructured radiology reports based on the presence of positive radiology findings (FPOS) and negative radiology findings (FNEG). The FPOS included new relevant radiology findings and any change in status from prior imaging. Electronic radiology reports from 1995–2002 and data from analysis of these reports with NLP-Leximer were saved in a data warehouse and exported to a multidimensional structure called the Radcube. Various relational queries on the data in the Radcube were performed using OLAP technique. Thus, NLP-OLAP was applied to determine trends of FPOS in different radiology exams for different patient and examination attributes. Pivot tables were exported from NLP-OLAP interface to Microsoft Excel for statistical analysis. Radcube allowed rapid and comprehensive analysis of FPOS and FNEG trends in a large radiology report database. Trends of FPOS were extracted for different patient attributes such as age groups, gender, clinical indications, diseases with ICD codes, patient types (inpatient, ambulatory), imaging characteristics such as imaging modalities, referring physicians, radiology subspecialties, and body regions. Data analysis showed substantial differences between FPOS rates for different imaging modalities ranging from 23.1% (mammography, 49,163/212,906) to 85.8% (nuclear medicine, 93,852/109,374; p < 0.0001). In conclusion, NLP-OLAP can help in analysis of yield of different radiology exams from a large radiology report database