More than one out four of the 1,364 organizations surveyed (27%), in fact, already use or plan to use cloud or software-as-a-service (SaaS) offerings to augment their core business intelligence (BI) functions. In addition, 17% indicate that they are even replacing at least some of their on-premises BI systems with cloud offerings.
Gartner offers three reasons why cloud resonates as a BI/analytics option among a growing number of companies:
Time to value: “The use of SaaS BI may lead to faster deployment, insight and value, particularly where IT is constrained by existing work and/or limited budget so that it cannot respond to demands for information and analysis as quickly as the business requires.”
Cost concerns: “The cost dynamic differs between on-premises and SaaS models. Software purchased as a service can usually be expensed, rather than capitalized, on the balance sheet. Buyers often think that SaaS is cheaper, but the reality is that this is unproven. Gartner’s cost models show SaaS can be cheaper over the first five years, but not thereafter. The long-term benefits lie elsewhere — in terms of cash flow, reduced IT support costs.”
Lack of available expertise: “SaaS analytic applications offer prebuilt intellectual property that can help firms work around a lack of the skills needed to build their own analytic solutions.”
Clinical data comes in all different forms and structures for the same piece of information. For example, age could be reported in years, or months or even in days. Without normalization i.e. converting disparate data into a single dataset view, it’s very hard to derive useful, meaningful clinical intelligence out of any medical data.
This not only applies to the data level attributes around patient but also the attributes that uniquely identifies the patient itself. The identifying attributes of patients such as any PII information (name, address, dob, ssn, insurer information, credit card, email, phone) could itself be presented in different formats and styles posing a real challenge linking all records back to one individual.
The other most important aspect that poses a huge challenge in clinical data normalization is around the fact that vast amount of information being stored in healthcare systems are in text format.
The above factors has been outlined just to give an idea of why clinical data normalization is unique and different compared to the traditional data normalization. It also shows the importance to build a generalized data normalization pipeline that takes into account not only the traditional variations with regards to transactions, numbers but also textual data that has key information to unveil the “real” clinical intelligence