Statistical Modelling for Medical Research
Data clinic applies biostatistics and the engineering of health informatics with the management of medical information to provide a pharmaceutical and bioengineering support function related to biostatistics and health information.
We assist in pharmacovigilance by providing both expertise and tooling for the collecting, monitoring, researching, assessing and evaluating information including quality based programme management and biostatistical (reputable) analysis. Pharmacovigilance services can be applied to both drug investigative studies on adverse effects and population trends on nominated cohorts.
Data Clinic has a broad number of advanced biostatistical methods available to assist in the modelling and biostatistical support for medical research. We can provide point in time assistance on individual research assignments or provide end to end depth across all biostatistics need to become your outsourced biostatistics partner.
High-level outcome studies can enable administrators to manage chronic diseases by indicators. Once direct data entry by providers of critical information such as medications and immunizations is implemented, an EMR's potential ability to perform any number of outcomes studies will ease the burden of conducting cost analyses and budgetary forecasts.
Data Clinic can assist in producing graphs, visual indicators and write up of final reports. Elements of the report can include:
- where EDA (exploratory data analysis) was used and how
- quality and measurement of the raw data
- empirical and qualitative methods used
- algorithms following standard practice
- describing the statistical methods with enough details to enable a knowledgeable reader with access to the original data to verify the reported results
- custom algorithms, why they were used, what auditing on the algorithms was provided to ensure clinical acceptability
- where derived variables were analysed
- where transformations were applied, and if so, why this was done
- where problematic values and transformations were encountered and how they are dealt with
- careful identification of subjective observations and opinions separated int he report where
How the EMR Helps With Medical Research
EMR statistics can be used to support life sciences programmes or be used by health organisations to help reduce medical errors, improve safety, increase screening and preventive care, reduce complications including drug errors, and facilitate the introduction of evidence-based guidelines.
To quote “Electronic Medical Records Could Help Find Cures, Speed Progress, Cut Costs”, one of the values of EMR statistical programmes is explained:
“Allowing for non-identified EMR sharing across the system creates a universal pool of data in which drug side-effects, treatment failure or success rates, disease history, specific organ damage or healing, and all sorts of incidence of drug interactions and health specifics can be cross-referenced, spurring a massive amount of data-rooted research and improving quality of care and treatment success rates.”
Access to relevant data and the ability to analyze large sets of statistically significant data could speed research and healthcare advancements by leaps and bounds.
You can link statistical EMR characteristics to genome or drug component database to provide detailed cross corelleraion analysis useful for medical research.
Examples of the attributes you can potentially use for EMR characteristics are:
DEMOGRAPHIC PROFILE
POPULATION SIZE
AGE STRUCTURE
PHYSICAL CHARACTERISTICS
INDIGENOUS POPULATION
FAMILY SIZE
FAMILY SITUATION
SETTLEMENT/MIGRATION TRENDS
ETHNICITY AND LANGUAGES SPOKE
INCOME AND BENEFITS
HOUSING
INDEX OF RELATIVE SOCIO-ECONOMIC DISADVANTAGE
EMPLOYMENT/UNEMPLOYMENT
LEVEL OF EDUCATION/SCHOOL RETENTION
EPIDEMIOLOGICAL
OVERALL HEALTH
CONTRIBUTORS TO THE BURDEN OF DISEASE
CHRONIC DISEASE FACTORS
INFECTIOUS DISEASE FACTORS
IMMUNISATION LEVELS
BREAKDOWN BY DISEASE CLASSIFICATIONS AND GROUPS
EMERGENCY ADMISSIONS TO HOSPITALS
LIFE EXPECTANCY AT BIRTH
AVOIDABLE MORTALITY
PREMATURE MORTALITY
DISABILITY
PSYCHOGRAPHICS
CULTURAL ATTITUDES
SPORTING ACTIVITIES
ACTIVITY IN INTERESTS AND OPINIONS ON HEALTH
POLITICAL SYSTEMS AND INCLINATIONS
CHARITY AND SOCIAL ACTIVITIES
ATTITUDES AND
COMMON VALUES
HEALTH & SOCIAL ISSUES
PROMOTING PHYSICAL ACTIVITY AND ACTIVE COMMUNITIES
PROMOTING ACCESSIBLE AND NUTRITIOUS FOOD
PROMOTING MENTAL HEALTH AND WELL BEING
SOCIAL INCLUSION
GEOGRAPHIC REMOTENESS
FREEDOM FROM DISCRIMINATION
FREEDOM FROM VIOLENCE
ACCESS TO ECONOMIC RESOURCES
REDUCING TOBACCO-RELATED HARM
REDUCING AND MINIMISING HARM FROM ALCOHOL
REDUCING AND MINIMISING HARM FROM DRUGS
SAFE ENVIRONMENTS
SEXUAL AND REPRODUCTIVE HEALTH
FIRMOGRAPHICS
LOCALE NEAR MINING OR HEAVY INDUSTRY
LOCALE NEAR FORESTRY
HISTORY AND PAST PERVALENCE OF ATTRIBUTES
AGE OF ESTABLISHED INFRASTRUCTURE-COMNUITY
AGE OF STAY IN LOCATION-FAMILY
TYPICAL NUBER OF COMPANIES/EMPLOYERS
TYPICAL SIZE OF COMPANIES/EMPLOYERS