Industry expertise and insight
Aculyst’s machine learning and big data consulting teams can help companies to deploy advanced analytics systems to solve some of the most difficult analytics problems in this day and age. Our skilled consultants have Doctorate Degrees in computer science, applied science which allows them to provide guidance and expertise in data governance management, data strategy, architecture modification recommendations, business transformation initiatives, deep learning, AI, machine intelligence tools and techniques to enable your company to extract compelling insights from your analytics database systems. There are several technologies, applications, software frameworks and platform vendors on the market to choose from, it can be a little overwhelming figuring out what to improve or change in your enterprise technology infrastructure.
Aculyst can provide consulting advice for IT departments who are managing, migrating big data workloads into cost-effective, high-performance hybrid cloud storage solutions to better store, collect, analyze their data to unlock new business insights, increase revenue, enhance customer experiences, improve products, and maximize operational efficiencies. Enterprise cloud service providers such a Microsoft Azure and Amazon AWS can support predictive analytics, distributed processing frameworks, real-time analytics, machine learning models, and large-scale data warehouses.
Healthcare Big Data Analytics Consulting
In the past half-decade advanced big data analytics techniques including data mining, natural language processing, predictive analytics and machine learning algorithms have been introduced into the healthcare industry to capitalize on the enormous amount of health data that has been made available. The availability of data has been helpful in improving the quality of personalized patient care, reducing costs, reducing medical errors, shrinking financial waste, curing illnesses, improve operational efficiency at hospitals, preventing insurance fraud. Big data initiatives may be implemented in many segments of the healthcare system including pharmacies, patients, payers and providers. It’s important when choosing a consultancy that they have expertise in both big data and knowledge of how the healthcare industry works with regards to patient care, healthcare management and governing regulatory laws such as HIPAA and the HITECH Act. Other examples include compliance with the Sunshine Act, which ensured that all payments made to healthcare providers on behalf of pharmaceutical companies became transparent. Other regulations allow the provider to opt-out of showing their prescription information to sales forces in pharmaceutical companies, while the fulfillment of orders may require that the same information be exposed to only a small group of non-sales individuals. Approved FDA drug representatives can engage doctors differently than drugs in the FDA approval process. It’s the reason there are data governance, compliance, and data security divisions within most pharmaceutical companies. Once one has developed a data strategy and taken the regulatory environment into account, it is imperative to develop the appropriate use cases, objectives, resources in order to develop the right analytics models, and then ensure that the insights provided do not run afoul of rules and regulations.
Healthcare specific big data analytics services may include:
- Predictive analytics and data mining solutions to personalize care in real time with the goal of pointing out the best practice treatments for patients afflicted with illnesses. It can improve preventative care by offering earlier detection and diagnosis in the disease symptoms process. An example of this is tumor genome sequencing.
- Help to better integrate, manage, store, and analyze both structured and unstructured data across a variety of sources (ranging from basic science, clinician records, and population demographics to community, environmental, behavioral, and wellness research data) for the purpose of making the data accessible to the doctors so they have the right information available in time to impact more patient outcomes.
- Get a more holistic view of patient care across a wide variety of procedures as well as conditions to spot crucial episode interdependencies.
- Use data mining to sift through enormous amounts of historical and unstructured data, to identify patterns and model certain scenarios in hopes of better-predicting events that might affect clinical wait times before delays occur.
- Help to improve key performance indicators for administrative, financial, and policy departments by integrating and analyzing data
- Integrating new data streams into an existing framework to provide a new revenue stream or to increase sales on an existing medication. For example, a mature prescription pharmaceutical drug marketing team could integrate patient test data into their prescriber data so that they can target which prescribers are more likely to have patients who test positive and are likely to be approved for their drug.