OBJECTIVES: Health care costs in Massachusetts (MA) are among the highest in the country. Thus, it is essential to gain an in-depth understanding of the patterns of healthcare resource utilization and expenditures in the state. This study examines healthcare spending in the state by care setting, and compares where most spending occurs for Medicaid and private payers. METHODS: We used the 2012 MA All Payer Claims Database, which included medical and pharmacy claims from all commercial payers and certain public programs (Medicare Part C only and Medicaid) to calculate healthcare utilization and expenditures for the state's population (N= 6,549,289 individuals), including patient out-of-pocket payments. Traditional Medicare claims were not included in this analysis. We conducted descriptive analyses to calculate and compare total annual healthcare spending by site of service for Medicaid and private payers. RESULTS: Total healthcare spending for MA in 2012 amounted to $25 billion for private payers and $10.3 billion for the state Medicaid program. For private payers, pharmacy claims accounted for 27% of total healthcare spending, and the top sites of service by spending were hospital outpatient (26%), hospital inpatient (19%), and office visits (15%). For Medicaid, the biggest contributors to healthcare spending were office visits (22%), followed by hospital inpatient visits (17%), skilled nursing facility visits (16% versus only 0.3% for private), and home health visits (14% versus 1.5% in private), with pharmacy claims comprising 12% of spending. CONCLUSIONS: We identified differences in patterns of healthcare resource utilization and expenditures between Medicaid and private payers. These differences reflect demographic and pattern of care differences in the insured populations, as well as different payment policies and prices paid for services. In order to improve care quality, equity, and efficiency, it is important to understand how money is being spent by different segments of the healthcare market. OBJECTIVES:The adaption of technological advances in the past decades underscores the importance of measuring how efficiently hospitals are utilizing the growing labor force to provide health services. Our study aimed to assess California hospital productivity growth through addressing the severity of patient's illness and outcomes of care. METHODS: We examined hospital productivity growth by analyzing patient discharge data from California for the period 2005 to 2011, among patient stays with a principal diagnosis of heart attack, pneumonia or heart failure. Productivity was defined by the ratio of the number of stays to total costs in each hospital-year. RESULTS: The study cohorts included 171,250 patient stays at 358 hospitals with a primary diagnosis of heart attack, 336,111 stays at 387 hospitals with pneumonia, and 389,413 stays at 383 hospitals with heart attack. Average costs per stay showed a slightly increasing trend from 2005 to 2011 (from $22,965 to $23,669 in heart-attack stays, from $10,956 to $12,2...
A81tool and disseminated it to a self-selected cohort of 11 small primary care practices that had previously achieved PCMH recognition from the NCQA. We assessed the cost of transformation between 2008 and 2011 using the tool. The cost of transformation was divided into four categories: the cost of NCQA patient centered recognition activities, the application cost of obtaining recognition, the cost of changes to practice culture, and the cost of external collaborations. Costs were averaged and weighted by the number of FTE providers in each practice in order to make the results comparable across practices. RESULTS: Three practices completed the tool. The weighted average cost of PCMH transformation was $35,508 per FTE provider in the year before recognition was achieved, and $38,218 in the recognition year itself. The most costly patient-centered activity (weighted average) in the pretransformation year was "providing self-care support" ($4,863/FTE provider), while "measuring and improving performance" ($9,503/FTE provider) was the most costly in the transformation year. CONCLUSIONS: The cost of recognition as a PCMH is a substantial but not insurmountable barrier to practice transformation. This information may be used by payers and policymakers to direct financial resources to primary care practices as they transform to the PCMH model. Indirect financial resources that assist in collecting cost data may also promote diffusion of the PCMH model.
and females had almost similar travel time of 2.43 and 2.40 minutes, respectively (p= 0.80). Travel time was 2.44 minutes for the patients younger than 65 years and 2.28 minutes for patients older than 65 years (p= 0.25). CONCLUSIONS: There was no major difference in access to pharmacy based on age or gender. Blacks have statistically significantly shorter travel time than whites. Future work will examine other factors like socioeconomic status.OBJECTIVES: The aim of the analysis was to compare the medicines centrally authorised by EMA with reference to their reimbursement in Poland. In Poland, innovative drugs need to be recommended by Agency for Health Technology Assessment (AOTM) before the decision about reimbursement is taken by Minister of Health. Recommendations issued by AOTM have been based on Manufacturer`s submission and additional officially published data, including EMA's data. METHODS: All decisions issued by EMA since 1995 connected with new medicines registration were analyzed and categorized into therapeutic area. Then it was checked which of the drugs found in the EMA's database were reimbursed in Poland in the years 2012-2013. RESULTS: It was found that till the end of December 2014 there were 563 unique active substance available in EMA's database. The analysis shows that in 2012 the reimbursement in Poland was related to 114 active substances registered in EMA, which is approx. 20% of all substances in the EMA's database. A year later 3% (23%) more active substances (131) were reimbursed in Poland. Most active substances registered in EMA and reimbursed in Poland, belongs to the group ATC: L (antineoplastic and immunomodulating medicines). Over 40% of the active ingredients of this group registered in EMA were reimbursed in Poland in 2012 and 50% in 2013. The second ATC group was A (alimentary tract and metabolism) -12% and group B (blood and blood forming organs) -11% (in both years). CONCLUSIONS: Not all drugs registered in EMA are reimbursed in Poland. We can conclude that anticancer drugs are the most likely to be paid from public sources, then medicines related to treatment metabolism and blood disorders. It was found that there was any drug reimbursed in Poland registered by EMA in ATC group P (antiparasitic products, insecticides and repellents).
A275was implemented. Tally sheets were used to categorize clinicians counter referral comments. Results: The study engaged 4 clinicians who made 132 counter-referral comments on referral slips delivered to them from CHWs. The comments were categorized into seven themes as indicated below. The theme "service provided and patient counter-referred to CHW" accounted for 40% (53/132); "continue with treatment" 16% (21/132);"medicine/treatment given" 15%(20/132); "patient advised to attend ANC, PNC and MCH/FP clinic" 12% (16/132); "patient recommended for further referral" 7%(9/132); "patient seen" 7% (9/132) and the theme " patient advised to come again" accounted for 3% (4/132.) ConClusions: Clinicians should take an active role in supporting and mentoring community health workers and ensuring that all members of households have access to healthcare. They need to recognize, appreciate and support their efforts. The referral and counter-referral comments made by both clinicians and CHWs acted as a perfect link between the two levels of healthcare.
A129 able in this large Medicaid cohort. Patients who used buprenorphine persistently for 12 months had lower risk of all-cause hospitalizations and ED visits than those experiencing early discontinuation.
The pharmacokinetic characteristics of tapentadol, marketed as Nucynta, put patients at higher risk of developing serotonin syndrome when prescribed concomitantly with other serotonergic medications. This research aims to examine the potential risk of serotonin syndrome by quantifying the concurrent use of tapentadol and other serotonergic medications. Methods: This was a retrospective cohort study. All patients with tapentadol claims were identified in the IMS Lifelink database from 2008 to 2014. The main outcome measure was the percentage of tapentadol patients with serotonergic medication usage overlap. This was compared to tramadol and oxycodone, opioids that have little or no association with serotonin syndrome. Statistical comparisons were performed using t-tests. Results: There were 9882 unique tapentadol patients, of which 5819 patients had indications for a serotonergic medication. 424 (7.29%) patients indicated for tapentadol and a serotonergic medication experienced an overlap. There were 266,132 unique tramadol patients, of which 117,862 (44.3%) have had an indication for a serotonergic medication. 7,032 (5.97%) of patients indicated for tramadol and a serotonergic medication experienced an overlap. A t-test showed that there was a significant difference between the percentages with a p-value of 0.0009. There were 472,999 oxycodone patients, of which 179,549 had indications for serotonergic medication. 5139 (2.86%) patients experienced overlap. ConClusions: Patients indicated for tapentadol and a serotonergic medication are more likely to experience an overlap than patients indicated for tramadol and a serotonergic medication. Due to this concomitant use, Tapentadol patients may experience a higher risk of developing serotonin syndrome. These findings can be used to help regulators recommend label changes for tapentadol and guide healthcare providers in prescribing decision-making in patients with indications for opioids and serotonergic medication.
(MOHP) sets pharmaceutical prices from ex-factory to retail. In July 2012, the pricing policy changed from a cost plus to an external reference pricing method which was effective in October 2012. Our goal was to identify the characteristics of products with price changes after the policy implementation. Methods: We used MOHP lists and IMS data to pre-identify products with price changes. METHODS: We used MOHP lists and IMS data to pre-identify products with price changes. In addition, purchase and sales data were obtained from a chain pharmacy in Alexandria for all transactions pre-and post-the policy change (April-Jun 2012 and 2013) to validate price changes, assess sales activity, and identify any additional products with price changes. Bivariate analysis and a logistic regression model were done to identify the determinants of price increase or decrease per Daily Defined Dose (DDD). RESULTS: A total of 206 products were subject to price changes; 66% of the products had price increase, 70% were generics, 36% were essential drugs, 40% of the products had prices less than 1EGP[1]/DDD, 30% were between1 and 5EGP/ DDD and 30% were higher than 5EGP/DDD. Half of the products were produced by domestic private companies, 27% by multinational firms, 21% by state-owned companies and 2% were imported. The products of state-owned firms had 23 times the odds of the products of multinational firms to have a price increase. Similarly, the cheapest products had 9 times the odds of a price increase compared to high priced products. Compared to brand name drugs, generics had 6.8 times the likelihood of a price increase. CONCLUSIONS: Being the product of State-owned firms, a product whose price was ≤ 1EGP/DDD or a generic were the main determinants of price increase.
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