The Best Approach to Clinical Data Management

Clinical Data Management is an important aspect of every healthcare organization. Clinical data is analyzed and processed in many ways. The major types of analysis are descriptive analysis, predictive analysis and simulations, and optimization. There are also different approaches to process and use data. In order to provide a more comprehensive description of clinical data management and it’s connection to data analytics, we will discuss these afro-mentioned concepts in detail below.

Descriptive Analysis

Descriptive analysis aims to understand an underlying phenomenon or process. Descriptive analysis answers questions like what options do customers usually choose together or which options usually never go together.

Predictive Analysis

Predictive analytics does the job of forecasting based on existing data. Predictive analysis studies the hidden relationships between factors and outcomes and then forecasts or estimates an unknown value. For example, a predictive model will allow us to predict which type of customers are going to show up even when the weather is bad or estimate how much revenue will be lost if temperatures drop 10 degrees.

Simulation and Optimization

Simulation imitates the operation and characteristics of a process and summarizes the outcome. Optimization prioritizes the decision options based on a key performance index. For example, if we want to design a drive-through route for a restaurant, we can simulate the traffic and ordering process, compare the simulation outputs for several options, optimize the design and select the best choice. Advanced analytics refers to quantitative methods such as statistics, algorithms and stochastic processes. Although not all of the advanced analytics techniques are predictive, they are future-oriented since the key idea of the methods is to support data-driven decisions in the future. The advanced analytics techniques can be categorized into three functions.

The Approach of Clinical Data Management

When it comes to clinical data management, there is a fundamental difference between traditional and advanced analytics, namely the process followed to design and solve a business problem. In traditional data management and also in traditional business intelligence, the analysis is typically built to be repeatable. IT develops the reporting template and extracts certain information important to the business in assessing historical performance. Thus, the types of information analyzed and the format in which the information is presented is predefined. Also instead of providing the data already processed and interpreted, the information technology department can also provide a platform with a user-friendly interface so hospital employees can utilize the data to answer their specific questions and interpret them the best way they see fit. Advanced analytics software vendors provide a friendly user interface to allow people with varied backgrounds to utilize the data to find the answer to their questions. Often the software will guide the user through the techniques by helping select and process the relevant information from multiple resources.

The Data Used in Clinical Data Management

In traditional data management, data that is usually gathered from a data warehouse for analysis. The analytics platform being used connects the data sources with the queries and then aggregates the data and puts them in hierarchy and categories. The front end users of this data such as business owners or healthcare specialists who wish to access this data can review the aggregated reports in order to understand historical operational performances. This process allows for in-depth observations to be made and if there is a discrepancy, further inquiries can be made and the data can be searched to the lower levels in order to find more details.

Advanced analytics for clinical data management ranges in use from large to small data sets. With the statistical methods and enhanced computational power, now businesses are able to capture the unique characteristics of each individual customers instead of analyzing customers at a segment level. So it becomes possible to make personalized marketing activities and improve marketing ROI (Return on Investment). The uses of this, of course, spread to other industries with unique customizations and nuanced uses.

Like the online social media community, uses it to manage unstructured data such as Social comments, images, and videos and user uploads that are also valuable sources of information. Stream computing, furthermore, helps people access and process real-time information. Organizations can monitor market sentiment and brand engagement to measure the effectiveness of a marketing campaign. They can also improve the product design by analyzing social comments. Thus, more complete, timely and various data are analyzed with advanced analytics.

In summary, while some of the underlying data may be consistent between traditional business intelligence and advanced analytics activities, these two techniques vary significantly in the “what”, “why”, “where” and “how” they are implemented. Advanced analytics wields the power to drive deeper, more strategic and more actionable insights from your data than traditional business intelligence reporting.