Benefits of Data Mashup

Data mashup is an increasingly important tool for businesses of all sizes, allowing users to gain new insights and spot trends within data. But what exactly is data mashup, and what makes it so valuable?

The term "mashup" emerged in the media-sphere within the past decade as people seized on the opportunities that new software and hardware technologies provided in combining different media together. However, in recent years, "data mashup" has become an important part of the BI industry, often referring to the combination of data from multiple separate sources into a data warehouse.

Going beyond this definition, InetSoft's platform offers the unique feature of enabling end-users to combine disparate data sources that are not already mapped within a data warehouse schema. This means that users can manipulate data from multiple sources without relying on the traditional middle step of ETL and data warehousing, and without even needing help from IT.

What makes InetSoft so valuable to businesses is that the queries that are needed to combine data are generated behind the scenes of a user friendly drag and drop interface. You simply enjoy the benefit of greater access to data, and greater flexibility in building interactive dashboards, compelling visuals, and intuitive reports.

About InetSoft

Since 1996 InetSoft has been delivering easy, agile, and robust business intelligence software that makes it possible for organizations and solution providers of all sizes to deploy or embed full-featured business intelligence solutions. Application highlights include visually-compelling and interactive dashboards that ensure greater end-user adoption plus pixel-perfect report generation, scheduling, and bursting. InetSoft's patent pending Data Block™ technology enables productive reuse of queries and a unique capability for end-user defined data mashup.

This capability combined with efficient information access enabled by InetSoft's visual analysis technologies allows maximum self-service that benefits the average business user, the IT administrator, and the developer. InetSoft was rated #1 in Butler Analytics Business Analytics Yearbook, and InetSoft's BI solutions have been deployed at over 5,000 organizations worldwide, including 25% of Fortune 500 companies, spanning all types of industries.

What Data Sources Does a Medical Analytics Impact & Outcomes Data Analyst Mash Up?

A Medical Analytics Impact & Outcomes Data Analyst typically mashes up data from various sources to analyze the impact and outcomes of medical interventions, treatments, and healthcare delivery models. These analysts leverage a wide range of data sources to gain insights into patient outcomes, healthcare quality, cost-effectiveness, and population health trends. Here are some common data sources that a Medical Analytics Impact & Outcomes Data Analyst may mash up:

1. Electronic Health Records (EHR):
   • Patient Demographics: Demographic information such as age, gender, ethnicity, and socioeconomic status.
   • Medical History: Clinical data including diagnoses, procedures, medications, laboratory results, and imaging studies.
   • Treatment Plans: Details of treatment plans, medications prescribed, and healthcare services provided to patients.
2. Healthcare Claims Data:
   • Insurance Claims: Claims data from healthcare payers including diagnoses, procedures, billing codes, and reimbursement amounts.
   • Utilization Patterns: Information on healthcare services utilization, including hospitalizations, outpatient visits, emergency department visits, and prescription drug usage.
   • Cost Data: Financial data related to healthcare costs, including payments to providers, insurance reimbursements, and patient out-of-pocket expenses.
3. Population Health Data:
   • Disease Registries: Registries containing information on specific diseases or conditions, such as cancer registries, diabetes registries, and chronic disease management programs.
   • Public Health Surveillance Data: Data from public health agencies and surveillance systems on infectious diseases, outbreaks, and health disparities within populations.
   • Socioeconomic Data: Socioeconomic indicators such as income levels, education levels, employment status, and housing conditions to assess social determinants of health.
4. Clinical Trials and Research Studies:
   • Clinical Trial Data: Data from clinical trials and research studies evaluating the efficacy, safety, and outcomes of medical interventions, treatments, and pharmaceuticals.
   • Research Databases: Access to databases and repositories containing medical research findings, systematic reviews, meta-analyses, and evidence-based guidelines.
5. Patient Surveys and Feedback:
   • Patient Satisfaction Surveys: Surveys and feedback from patients regarding their healthcare experiences, satisfaction levels, and perceived quality of care.
   • Patient-reported Outcomes: Data on patient-reported outcomes, quality of life measures, symptom severity, and functional status collected through surveys and questionnaires.
6. Healthcare Provider Data:
   • Provider Performance Metrics: Performance data on healthcare providers, including hospitals, physicians, clinics, and healthcare facilities.
   • Credentialing and Licensure Data: Information on provider credentials, licensure status, specialty certifications, and professional affiliations.
7. Health Information Exchange (HIE):
   • Interoperability Data: Data exchanged between healthcare providers, healthcare organizations, and health information exchanges to support coordinated care, care transitions, and data sharing.
   • Continuity of Care Documents (CCDs): Structured documents containing patient health information shared between providers to facilitate care coordination and information exchange.
8. Healthcare IT Systems:
   • Electronic Medical Records (EMR): Data from EMR systems used by healthcare providers to document patient encounters, clinical notes, and treatment plans.
   • Practice Management Systems: Administrative data from practice management systems used for scheduling appointments, billing, and revenue cycle management.
9. Pharmaceutical and Medical Device Data:
   • Drug Registries: Registries containing information on pharmaceuticals, including drug utilization, adverse drug events, and medication adherence.
   • Medical Device Data: Data on medical devices, including usage patterns, safety reports, and post-market surveillance data.
10. External Data Sources:
    • Social Determinants of Health: Data from external sources such as census data, public health surveys, and social service agencies to assess social determinants of health and disparities.
    • Environmental Data: Environmental data such as air quality, water quality, and climate data to analyze environmental impacts on health outcomes.