Learning Health Systems in Australia Analysis

Learning Health Systems in Australia Analysis Submitted by : Jaison Prabhath Jaiprakash INTRODUCTION A Learning Health System (LHS) aims to deliver the best possible care to patients, each time, and to learn and improve itself with each care experience. Its vision guarantees to change healthcare services, by empowering the health professionals to change the entire health care system into a highly reliable industry. A learning health system combines quality patient care with the routine collection of data. This is aimed at improving patient outcome. A fully functional system like this would advance the overall quality of healthcare and improve patient and provider safety. The data collected through electronic health records are vast and expanding, which helps in creating new knowledge about the effectiveness of the given treatment and helps in predicting outcomes. An LSH emphasises on an approach that shares data and insights across boundaries to drive better, more efficient medical practice and patient care. The key to achieve their objectives are linked to the collection of data th at is commonly called Big Data from various types of clinical practices. The big data movement in computer science has brought dramatic changes in what counts as data, how that data is analysed, and what can be done with that data. Big data has only recently begun to influence clinical practice. (Iwashyna and Liu, 2014). Enormous amounts of health care data are collected from patients and populations and the interpretation of that data is very important in meeting the needs of the patients. Combining big data and next-generation analytics into population health research and clinical practice requires new data sources, new thinking, training, and tools. If properly used, these pools of data can be an infinite source of knowledge to power a learning health care system. Clinical trials help to manage and improve the health care system. It is all about conducting studies and investigations into various diseases and conditions and eventually hope to eradicate the illnesses. It helps to harness the information for improved clinical trial design, patient recruitment, site selection, monitoring insight and decision making. Data produced through clinical trials like randomized control trials (RCT) often include many treatments and patients from different groups, to improve the reliability of participants and to access the data, these records are digitized, this is where big data helps to store large amount of data sets. By mining the area of clinical practice, we can learn a lot about the patient care. METHODS Search Strategy The SCOPUS and PubMed databases were searched for articles related to the role of learning health systems and clinical practice. Most articles were taken from the year 2014. The search was limited to articles published in journals. Search terms A Boolean search was performed using the following terms: learning health system AND clinical practice, learning healthcare system AND clinical practice, learning health system AND clinic and learning healthcare system AND clinic. Selection / inclusion Criteria The literature review was conducted and articles chosen were from the existing learning health systems such as PEDSnet which are already being used for various clinical practices.   The search was later filtered into aspects that are essential to clinical practice as well as learning health systems, namely, big data. RESULT The role of the health care system is important to deliver the quality care and treatment to the patients. Learning health systems have shown remarkable developments in clinical practices, for example formation of Clinical Data Research Networks (CDRN) consist of many health care systems which conducts research as a network on topics like health care delivery, population health, assessing health disparities and so on. A few of these healthcare systems are listed below. PEDSnet: A National Pediatric Learning Health System   Ã‚   PEDSnet is a clinical data research network (CDRN) that provides the infrastructure to support a national paediatric learning health system. The PEDSnet clinical data research network is an association of eight childrens hospitals, two existing patient-centred disease-specific paediatric networks addressing inflammatory bowel disease and complex congenital heart disease, a newly formed paediatric obesity network, and two national data partners. Together they form the essential components of the National Paediatric Learning Health System (NPLHS). The NPLHS will establish the data sharing environment to enable a community of patients and clinicians, interacting at the point of care, to generate data that can be reused for research and quality improvement and to support continuous monitoring of outcomes that identify specific management practices as targets for comparative effectiveness research (CER). (Forrest et al., 2014) All the information about the patients are recorded using Patient Reported Data (PRD) for quality improvement, clinical practice, or research applications. Table 1: PEDSnet overview (Forrest et al., 2014) Point of Care Research (POC-R) Point of Care Research (POC-R) is a clinical study design that is used to compare two or more treatments that are considered equal. It takes advantage of Electronic health records to enable participant recruitment and data collection of the patients. The goal of POC-R is to embed research into clinical practice, contributing to a Learning Healthcare System (Weir et al., 2014). pSCANNER (part of the PCORnet) The patient-centred Scalable National Network for Effectiveness Research (pSCANNER), is a part of the recently formed PCORnet (Patient Centred Outcomes Research net), which is a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centred Outcomes Research Institute (PCORI). Its mission is to provide health related data available to clinicians, researchers and other stakeholders to improve the health-related policies, decision-making and governance. It uses a distributed architecture to integrate data from three existing networks VA Informatics and Computing Infrastructure (VINCI), University of California Research exchange (UC-ReX) and SCANNER, a consortium of UCSD covering over 21 million patients in all 50 states of the USA providing ambulatory care and community-based outpatient clinics with claims and health information exchange data. (Ohno-Machado et al., 2014). pSCANNER shares the data but also protects the privacy of patients at the same time. Only summary statistics are shared between the researcher and clinician. Initial use cases will focus on three conditions: congestive heart failure, Kawasaki disease and obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses. (Ohno-Machado et al., 2014) Learn From Every Patient (LFEP) The merging of three major trends in medicine, namely conversion to electronic health records (EHRs), prioritization of translational research, and the need to control healthcare expenditures, has created unique interests and chances to develop systems that advance healthcare while reducing the overall cost. But making a learning health system operational requires regular changes that have not yet been widely demonstrated in clinical practice. The authors developed, implemented, and evaluated a model of EHR-supported care in a cohort of 131 children with cerebral palsy that integrated clinical care, quality improvement, and research, entitled Learn from Every Patient (LFEP). Children treated in the LFEP Program for a 12-month period experienced a 43% reduction in total inpatient days, a 27% reduction in inpatient admissions, a 30% reduction in emergency department visits, and a 29% reduction in urgent care visits. LFEP Program implementation also resulted in reductions in healthcare costs of 210% (US$7014/child) versus a Time control group, and reductions of 176% ($6596/child) versus a Program Activities control group. Importantly, clinical implementation of the LFEP Program has also driven the continuous accumulation of robust research-quality data for both publication and implementation of evidence-based improvements in clinical care. These results demonstrate that a learning health system can be developed and implemented in a cost-effective manner, and can integrate clinical care and research to systematically drive simultaneous clinical quality improvement and reduced healthcare costs. (Lowes et al., 2017) Figure 1: The Learn From Every Patient (LFEP) model PaTH PaTH provides an informatics supported infrastructure for cohort identification and data sharing within the network of three targeted conditions: idiopathic pulmonary fibrosis (IPF), atrial fibrillation (AF), and obesity. It helps in linking the electronic patients records and understand the survey methods used in research. It uses an open source tools (i2b2 and SHRINE) to aggregate, analyze the distributed data, and facilitate patient centered, comparative effective research. It also helps in improving the decision making capability of both patients and physicians through collaborative process that brings each partner closer to the ideals of a learning health system. (Waqas Amin, 2014). DISCUSSION Big Data is an important but diverse intellectual movement seeking to bring new technologies of data acquisition, data integration, and data analysis into clinical research, hospital operations, and clinical practice. These trends will only accelerate for the foreseeable future, as they build on decades of others doing exactly those same things. Big Data will not solve fundamental challenges of either logical inference or of human behaviour. (Weir et al., 2014). Big Data will continue to provide new knowledge and decision-making support for an array of real and pressing clinical problems (Iwashyna and Liu, 2014). PEDSnet will transform paediatric healthcare and childrens health by developing an extensive and efficient digital infrastructure that enables all participants to work together in the work of producing new knowledge and improving health and care delivery. PEDSnet benefits from robust pre-existing resources and a unique history of collaboration by childrens hospitals that has fundamentally reshaped outcomes for previously fatal diseases, such as cystic fibrosis and many childhood cancers. As the basic digital structure to a learning health system, PEDSnet enables the quick application of new evidence into clinical practice and will address fundamental questions of clinical effectiveness for children and their families, particularly for individuals affected by serious, and generally rare, illness that persists into adulthood. (Forrest et al., 2014) The Point of Care Research (POC-R) highlights several possible factors important to a nationwide implementation of a pragmatic trial program. Participants were significantly concerned with added burden, changes in the provider-patient relationship, ethical implications, validity of results, and integration with workflow. To encourage and support provider buy-in, programs might consider provider training, marketing, and electronic support for decision-making. Providing evidence of equipoise and the validity of data capture might be essential for buy-in. Work process analysis should be part of the proposal. (Weir et al., 2014) pSCANNER will encode a significant portion of policies in software, use a flexible strategy to harmonize data, and use privacy-preserving technology that enables highly diverse institutions to join the network and allow stakeholders to participate. Significant challenges in terms of providing sufficient incentives for patients, clinicians, and health systems to participate and ensuring the sustainability of the network, which were not the focus of this article, will also need to be addressed. The pSCANNER project offers a unique opportunity to make progress toward these objectives, and share results with a community of researchers and representatives from a broader group of stakeholders. (Ohno-Machado et al., 2014) The introduction of EHR-supported care that integrated clinical care, quality improvement, and research resulted in large reductions in healthcare utilization, with associated reductions in charges. Direct comparisons with two distinct comparison groups, to account for the effects of time and LFEP Program activities, confirmed that patients in the LFEP Program had greater reductions both in healthcare utilization and healthcare charges than either control group. Together, these early results confirm that it is both feasible and cost-effective to operationalize key components of an LHS in a large academic medical center. Furthermore, such a system is able to simultaneously improve clinical care and efficiency, and reduce healthcare expenditures, while creating a robust research-quality data set enabling healthcare systems to systematically Learn from Every Patient. (Lowes et al., 2017) The PaTH network will adhere to best practices by using as its backbone open source tools (i2b2 and SHRINE) to aggregate data using standard vocabularies and provide distributed, de-identified cohort queries. PaTH will test these systems in three targeted disease conditions. PaTH will provide a robust informatics supported platform to facilitate comparative effectiveness research, support the conduct of clinical trials, and improve the decision-making capability of both patients and physicians through a collaborative process that brings each partner closer to the ideals of a learning health system. (Waqas Amin, 2014) CONCLUSION The ongoing feedback of insights from data to patients, clinicians, managers and policymakers can be a powerful motivator for change as well as provide an evidence base for action. Many studies and systems have demonstrated that routine data can be a powerful tool when used appropriately to improve the quality of care. A learning healthcare system may address the challenges faced by our health systems, but for routinely collected data to be used optimally within such a system, simultaneous development is needed in several areas, including analytical methods, data linkage, information infrastructures and ways to understand how the data were generated. (Deeny and Steventon, 2015) These results demonstrate that a learning health system can be developed and implemented in a cost-effective manner, and can integrate clinical care and research to steadily drive simultaneous clinical quality improvement and reduce the overall cost of healthcare. (Lowes et al., 2017) REFERENCES BRODY, H. MILLER, F. G. 2013. The Research-Clinical Practice Distinction, Learning Health Systems, and Relationships. Hastings Center Report, 43, 41-47. DEENY, S. R. STEVENTON, A. 2015. Making sense of the shadows: Priorities for creating a learning healthcare system based on routinely collected data. BMJ Quality and Safety, 24, 505-515. FORREST, C. B., MARGOLIS, P. A., CHARLES BAILEY, L., MARSOLO, K., DEL BECCARO, M. A., FINKELSTEIN, J. A., MILOV, D. E., VIELAND, V. J., WOLF, B. A., YU, F. B. KAHN, M. G. 2014. PEDSnet: A national pediatric learning health system. Journal of the American Medical Informatics Association, 21, 602-606. GRANT, R. W., URATSU, C. S., ESTACIO, K. R., ALTSCHULER, A., KIM, E., FIREMAN, B., ADAMS, A. S., SCHMITTDIEL, J. A. HEISLER, M. 2016. Pre-Visit Prioritization for complex patients with diabetes: Randomized trial design and implementation within an integrated health care system. Contemporary Clinical Trials, 47, 196-201. IWASHYNA, T. J. LIU, V. 2014. Whats so different about big data?: A primer for clinicians trained to think epidemiologically. Annals of the American Thoracic Society, 11, 1130-1135. LOWES, L. P., NORITZ, G. H., NEWMEYER, A., EMBI, P. J., YIN, H., SMOYER, W. E., LEARN FROM EVERY PATIENT STUDY, G., TIDBALL, A., LOVE, L., SCHMIDT, J., GOLIAS, J. MILLER, M. 2017. Learn From Every Patient: implementation and early results of a learning health system. Developmental Medicine and Child Neurology, 59, 183-191. OHNO-MACHADO, L., AGHA, Z., BELL, D. S., DAHM, L., DAY, M. E., DOCTOR, J. N., GABRIEL, D., KAHLON, M. K., KIM, K. K., HOGARTH, M., MATHENY, M. E., MEEKER, D. NEBEKER, J. R. 2014. pSCANNER: Patient-centered scalable national network for effectiveness research. Journal of the American Medical Informatics Association, 21, 621-626. STEINER, J. F., SHAINLINE, M. R., BISHOP, M. C. XU, S. 2016. Reducing missed primary care appointments in a learning health system. Medical Care, 54, 689-696. WAQAS AMIN, F. R. T., CHARLES BORROMEO, CYNTHIA H CHUANG, 2014. PaTH: towards a learning health system in the Mid-Atlantic region. Journal of the American Medical Informatics Association, 21, 633-636. WEIR, C. R., BUTLER, J., THRAEN, I., WOODS, P. A., HERMOS, J., FERGUSON, R., GLEASON, T., BARRUS, R. FIORE, L. 2014. Veterans Healthcare Administration providers attitudes and perceptions regarding pragmatic trials embedded at the point of care. Clinical Trials, 11, 292-299.

Background/Introduction of Wal-Mart Germany: A Failed Marketing Plan

Wal-Mart is not only the world’s most dominant player in the retail home market industry, it is also the world’s largest corporation in terms of revenue earning more than $240 billion in 2003. It is also the biggest private-sector employer in the world today with around 1. 38 million staff on its payroll. The first Wal-Mart was set up in 1962 by brothers Sam and Bud Walton as a five and dime store in Rogers, Arkansas. Forty years later, branches have mushroomed all over America.Today there are 1,647 Discount Stores, 1,066 Supercenters, 500 SAM’s clubs and 31 Neighborhood Stores in operation across the country—all under the Wal-Mart corporate umbrella. Wal-Mart thrives on its everyday low prices (made possible by its sophisticated inventory management system and the biggest private satellite communication system in the world), emphasis on customer service, and highly-motivated personnel. With its huge and uncontested success in the homeland, Wal-Mart decide d in 1991 to embark on an ambitious campaign to become an international retail store corporation.Its goal was to have its international operations contribute a third of its total profits by 2005. It opened a SAM’s club outfit, its first overseas branch, in Palenco, Mexico City. Since then, Wal-Mart has opened branches in 9 countries and in 1993, it opened the Wal-Mart International Division, to oversee the company’s international operations. So far, revenue returns has been spectacular. In 1979, its annual turnover reached $1 billion for the first time. In 1993, it earned a billion in only a week and in November, 2001, in a record-breaking single day. In the year ending January 31, 2003, Wal-Mart posted sales of $244.5 billion, with about 16. 5% earned abroad. Its 2003 turnover is three times higher than Carrefour’s, the world’s second biggest retailer. However, while Wal-Mart has become the market leader in the US, Mexico and Canada, the same didnâ€℠¢t hold true for its other overseas markets. Its operations in Asia (which includes China, South Korea and Japan) and Latin America (Brazil and Argentina) are profitable but not as much as the North American profit rates. A notable case to consider, however, is Wal-Mart’s failure in the German market. The Wal-Mart Germany FiascoGermany is the third biggest retail market in the world after US and Japan. In December 1997, Wal-Mart decided to expand into Germany—a move that was once considered as an initial foray to make its presence known throughout Europe. The company took over the chain of retail stores, Wertkauf, for about $1. 04 billion and Interspar hypermarkets for â‚ ¬560 million. However, revenues have not mirrored those of North American postings. By 2002, Wal-Mart Germany only earned an estimated â‚ ¬2. 9 billion, a market share of 1. 1%. By 2003, it has lost about â‚ ¬1 billion, closed two outlets and laid-off around 1,000 staff.Wal-Mart’s Germ an operations is said to have failed because of four reasons: First, Wal-Mart’s entry into the German market was through acquiring 74 Spar hypermarkets, a company which before the buyout was already the weakest player in the market. Spar stores were located in less well-off areas and has the industry’s highest logistics cost and lower returns. Meanwhile, its acquisition of Interspar is considered as an overpriced deal since the same chain of stores were bought by its former company only two years earlier at a price seven times lower than what Wal-Mart had to pay for.The second reason is the clash of cultures between Wal-Mart Germany’s American CEOs and German employees. The ignorance of these executives regarding Germany’s laws and culture has created widespread employee dissatisfaction and union-bashing. American Rob Tiarks, Wal-Mart Germany’s first CEO, was unwilling to learn the German language, ignorant with the country’s framework of re tail market and ignored the strategic advice given to him by former Wertkauf executives. The company installed a German CEO in 2001 but his ability to turn Wal-Mart’s future around is yet to be judged.It also has to deal with unions, a factor that is absent in its US operations. Third, Wal-Mart has not been able to deliver its promise of lower prices and compete with other and bigger discount stores in the country like Aldi. German shoppers have also been turned-off by the concept of â€Å"greeters† which, in America, is considered good customer service but a form of harassment in the European country whose people are used to self-service. It also cannot offer the 24/7 convenience of its American store counterparts because of Germany’s restrictive shopping hour regulations.Finally, Wal-Mart Germany has been continually accused of infringing German laws and regulations like the anti-trust act which requires all corporations to disclose financial information. More problems could be foreseen for the company using the present situation as gauge. So far, it has failed to accomplish the financial benchmarks it has set for its first European foray. The future of Wal-Mart Germany is, indeed, not encouraging at this point in time. Reference Knorr, Andreas and Andreas Arndt. Why did Wal-Mart Fail in Germany? Bremen: Institute for World Economics and International Management, June 2003.

Law of Torts

PAPER-4 (LL1008) LAW OF TORT AND CONSUMER PROTECTION LAWS nd st (2 Semester, 1 Year of the 3-Year LLB course) PART A- Law of torts PART B – Consumer Protection Law PART –A General Principles 1. General Principles – Definition, distinction between tort, crime, contract, breach of trust. 2. Essential conditions of liability – Damnum Since injuria, Injuria sine damnum, Malice, Motive. 3. Foundations of tortuous liability, fault liability, strict liability, principles of insurance in torts. 4.Capacity of parties to use and / or be used – State its subordinates – executive officers, judicial officers – mirrors corporation, unicorporated bodies, trade unions, etc foreign soveriengs, convicts bank rupts. 5. General defences – consent, resources cases, inevitable accident, Act of G mistake private defence, necessity statutory authority, act of State. 6. Remedies – Judical and extra judicial Damages- kinds of damages remoteness of damages- comparison with principles in contracts ; novas actus intervenes, successive action on the same facts, Merger of tort in felony. . Vicarious liability – Master and Servant – Distinction between servant and independent contractor, concept of servant, course of employment, Hospital cases, Masters duties towards servants, servants duties to his master, Servant with two masters, common employment, liability for tort of independence contractors, criminal acts of servants. 8. Joint tort feasors – common law rules, law reform act, 1935 applicability in India of the English principles. 9. Effect of death of parties in tort – Motor Vehicle accident cases. Specific Torts : 10.Wrong to person – assult, battery, false imprisonment 11. Wrongs to property – trespass to land, continuing trespass, trespass to goods, convesion, detinue specific restitution. 12. Wrong to reputation – defamation – libel, slander- principles governing li ability for defamation; defences – Justificaiton fair comment principles – absolute and qualified. 13. Negligence – Proof of negligence principle in Donoghue Vs Stevenson, requirements standard of care, resipsa loquiture contributory negligence, principles in Devies Vs Mann the last opportunity rules- constructive last opportunity rule. 4. Dangerous chattels – duty to persons permitted or invited to use chattel duty to immediate and ultimate transferee.23 15. Deceit- rule in Derry Vs Peek, principles of liability, exceptions – liability for negligent mis statement. 16. Injury of Servitudes, Nuisance, Private and Public – defences valid and invalid 17. Occupier’s liability – (1) under a contract (2) as invitee (3) as licence (4) as trespasser (5) Child Visitor. 18. Conspiracy – requirements 19. Injurious falsehood – slander of title, slander of goods – passing off interference with freedom of contract, intim ation. 0. Wrongs of family relations – husband and wife, parents and child, seduction – enticement, loss of service. 21. Strict liability- rule in Rylands Vs Fletcher, exceptions to the rule, liability for animals, cattle trespass. 22. Abuse of legal process – malicious prosecution, malicious civil proceedings, maintenance and champerty. New and emergent torts (Pages 36 to 43 of Salmond, 20th ed. And pages 324 to 327 of Pillai – 8th Edition) Books for Reference 1. 2. 3. 4. 5. 6. 7. Salmond Winfield Ratanlal Ramaswamy Iyer Gandhi B. M.Achutan Pillai James Law of Torts Law of Torts Law of Torts Law of Torts Law of Torts Law of Torts Introduction of the Law of Torts Cases to be studies 1. Ashby Vs White : Smith leading cases 266 912 ed (Legal Damages) 2. Mayor of Bradford Vs Pickles : (1895) AC 587 (Malic – relevancy) 3. Haynes Vs Harwood (1935) 1 KB 146 (Rescue Cases) 4. Stanley Vs Powell : 11 (1891) 1 OB 86 (Inveitable accident) 5. Bird Vs Hallorook (1828) 4 Bing 628=861 of Morrison – case on Torts (No duty to trespasser) 6. Bird Vs Hallorook (1828) 4 Bing 628=861 of Morrison –case on Torts (No duty to trespasser) 7.Buron Vs Denman (1848) 2 Ex 167 (Act of State) 8. Mersey Docks & Harbour Board Vs Coggins and Griffiths : (1946) 2 ALER 345 (Liability of the servant lent to another) 9. Llyod Vs Grace, Smith and Co (1912) 1 ABD 814 (Liability for projection over highway and for independent contracts act) 10. Tarry Vs Ashtorf (1876) 1 ABD 814 (Liability for projection over highway and for independent contractors acts) 11. Kasturilal and Ralia Ram Vs State of UP AIR 1965 SC 1039 (Governments liability for torts of its servants) 4 12. Wilsons and Clyde coas Co Vs English (1938) AC 57 = (1973) 03 All ER 628 (Masters duty to servants) 13. Polemis and furness with and Co (1921) 3 KB 560 (Fest Remoteness) 14. Over seas Tankshop (JK) Ltd Morts Dock and Engineering Co (1961) AC 388 = (1961) 1 ALL ER 494 (Tests of remoteness o f damage) 15. Rose Vs Ford (1937) AC 826 (1937) 3 ALL ER (359) Damages for loss of expectation of life. 16. Bird Vs Jones (1845) 7 AB 742 temporary false imprisonment (1912) KB 496 (necessity as a justification) 17.Six carpenters case (1610) 8 Co Rep 146 on Smith leading cases Vol 1 P 127 (Tress ab initio) 18. Cassidy Vs Daily Mirror News papers Ltd (1929) 2 KB 331 (defamation unintentional publication) 19. Blyth Vs Birmingham Water worked Co (1856) II Ex 781 (Definition of negligence) 20. Donoghue Vs Stevenson (1932) AC 562 (damages for breach of duty of care negligence) 21. Davies Vs Mann (1842) 10 546 or Morrison cases on torts 688 (last opportunity rules) 22. British Columbia Electric Railway Vs Loach (1916) 1 AC 759 (Constructive last opportunity rules) 23.Hambrook Vs Stroke Brothers (1925) 1 KG 141 (Nervous Shac) 24. Derry Vs Peek (1889) 14 AC 337 (deceit requirements of) 25. Hedley Byrne and Co ltd Vs Heller and Partners Ltd (1963) 2 ALL ET 575 (Liability for negligent mis st atements) 26. Francis Vs Cockrel (1870) LR 5 OB 591 (concept of dangerous premises) 27. Fairman Vs Peretuall investment building society (1923) AC 74 Occupoiers duty to licences) 28. Indermour Vs Dames (1866) LR 1 CP 274 (Occupiers liability to persons entering under contract) 29.Cooke Vs Midland Great Western Railway of Ireland (1909) AC 229 (Occupers liability to children) 30. Crofter Hand Waven Harris Tweed Co Ltd Vs Veitch (1942) AC 435 = (1942) 1 ALL ER 142 (Conspiracy) 31. Lubley Vs Gye (1853) 2 Ed 216 (introducing a breach of contract) 32. Rylands Vs Fletcher (1868) LR 3 HL 339 = Smiths leading cases Vol 278 (Strict liabiolity principle) 33. Read Vs Lyons & Ltd (1945) KB 216 = (1945) 1 ALL ER 106 (escape necessary for strict liability) 34. May Vs Burdett (18460 9 AB 101 (Liability for animals) 25 PART- B ( 2nd Semester, 1st Year of the 3-Year LLB course)CONSUMER PROTECTION LAW Consumer Protection Act – 1986- Definitions, consumer Protection Councils, their objects â₠¬â€œ consumer Disputes Redressal agencies – District forum, State Commission, National Commission- their jurisdiction, constitution, powers, procedure – appeals, reliefs to the parties, enforcement of the orders. Reading materials 1. Consumer Protection Act 1986 2. Law of Consumer Protection 3. Law of Consumer Protection 4. Law of Consumer Protection 5. Law of Consumer Protection Gurubax Singh D. N. Saraf R. K. Bangia Kaushal

Personal Plan to Succeed - 1077 Words

Personal Plan to Succeed Katrina Lino HSC/504 November 5, 2012 John Dean Personal Plan to Succeed As age increases, so do the number of responsibilities assumed in our lives. We have careers, family that needs taking care of, and homes that need maintenance. These responsibilities can be obstacles when returning to school and obtaining a higher degree such as a Master’s of Science. Although there are challenges, the drive for higher learning never subsides in some people. In this paper, I will review my reasons for continuing my education and obtaining my Master’s degree in Nursing and discuss my short- and long-term goals as well as what challenges I may face to achieve these goals. I will also analyze my strength and weakness in†¦show more content†¦Because of the challenges I am facing on the road to obtaining an MSN degree, I have decided to pursue the degree using online education. This online program tract offered by University of Phoenix is ideal for my personal situation. However, working completely online will affect person al communication with my professor and classmates. We will communicate on an online forum. In a classroom setting, my personal communication strength is non-verbal cues. When I am expressing a thought, I use facial expressions and I am receptive to cues exhibited from the audience. However, in an online setting, non-verbal cues are not used. Personalization is lost when using the Internet to convey a message (Todericiu, Muscalu, amp; Fraticiu, 2012) I must use writing skills to convey my ideas with the class. This is considered my weakness when communicating via online with professors and fellow classmates. To improve my personal communication, three strategies will be implemented. The first strategy is by being clear and concise in my writing. Second, prior to posting on the forum, I will organize my ideas so that the message is delivered effectively. Last, in a classroom setting, I would be an active listener. 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As well acknowledge my need to stay motivated in order to complete and succeed in my academics. By having a support system using the resources provided by my educational institute. Successful education is having intricately