1. Briefly explain the differences between BI and DSS? Business intelligence (BI) systems monitor situations and identify problems and/or opportunities, using analytic methods. However, Decision Support Systems (DSS) are typically built to support the solution of a certain problem or to evaluate an opportunity, already identified. BI systems are not only for decision support but a very important feature they have is: reporting. Thus, DSS directly supports specific decision making, however, BI provides timely and accurate information that indirectly supports decision making.All BI systems include four components: a data warehouse (source data), business analytics (collections of tools for manipulating, mining and analyzing data), business performance management (monitoring and analyzing performance) and a user interface (dashboards, digital cockpits, etc.) However, a DSS may have its own database and thus are known as DSS applications.BI systems include tools for querying and reporting, text and data mining, digital cockpits, dashboards and scorecards, alerts and notifications, and many more applications and features recently developed and continuously being added under this umbrella term. Certain DSS are also included in BI. The components of DSS include a data management subsystem, model management subsystem, knowledge-based management subsystem and user interface subsystem. Relationship between different types of DSS and BI is represented in the figure shown above. In essence, the process of BI is based on the transformation of data to information, then to decision and finally into actions whereas DSS uses data to support decision making only. BI has executive and strategy orientation and is more useful for business leaders whereas DSS is more oriented towards analysts. This is because of the very nature of data visualization capabilities that BI has. Business leaders do not analyze all available data personally but take decisions on analysis done by middle management. To understand these analyses BI tools of visualization come in handy. Sometimes, they would want to dig deeper into a particular problem and thus scalable data mining capabilities embedded in BI systems facilitate detailed analysis of data. BI systems are developed by commercially available tools, however, DSS needs higher programming expertise to tailor make it for specific organization purposes and thus are developed mostly in academic world. This is in contrast to BI systems which are typically developed by software companies.

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2. Read the article Predictive Analytics Saving Lives and Lowering Medical Bills. Summarize how it suggests predictive analytics can be used to save lives and lower medical bills.

The problem: Medication prescription non-adherence among US citizens. This costs $290 billion per year (avoidable costs) including medical, hospitalization and surgery costs.Why predictive analytics: Most existing strategies are based on doctors observing negative health consequences after patients have stopped taking their meds. By then it is too late. A retrospective approach descriptive or prescriptive analytics does not work. This is why predictive analytics can help address this issue.How? By alerting doctors, pharmacists and health plans about patients who are most vulnerable to non-adherence, preventive measures can be taken before patients experience negative health outcomes. FICO Medication Adherence Score: Collection of publicly available data including fields like patients age, gender, marital status, time in their current residence, geographic region and disease can predict behavior about medical adherence.Data scientists built a model (with a range of 1500) indicating the probability of a patient adhering to the prescription in the first year of therapy. The random sample included more than one million patients who had been diagnosed with one of the following five diseases: asthma, depression, diabetes, high cholesterol or hypertension. The scientists then analyzed these five data sets by using pattern recognition methodology, between patients who filled/refilled prescriptions and those who didnt. From there they identified the variables most associated with medication adherence and developed different prescription-adherence models for each of the five diseases. On an average a patient who fell in the top decile of this model adhered to their respective prescriptions for 129 days more than the ones who did not. From this data analysis, doctors and health insurers can use different tactics to help patients adhere to their prescriptions. These tactics can be: reminders, simpler drug regimes, cheaper drug programs, overcoming language barriers and in extreme cases nurses visiting the patients personally. Another analysis helped in segmenting patients in six groups according to how they could be engaged and communicated with more effectively. The result of this program was that the medication adherence among the test subjects went up by 36%.