Data Science and Dental Science a Case study

1. DentaQuest : Data Science
Data Science in Dental Healthcare:
Dental Hygiene parameters like cleanliness/hygiene of “ Lips, tongue ,Gums and tissues, Saliva,
Natural Teeth, Dentures, Oral Cleanliness, Dental pain Can be used to predict outcomes like
diseases from medical-dental electronic health record enterprisedatawarehouse presentsagreat
opportunitytoconductepidemiologic,outcomes-based,andcomparative effectivenessstudiesin
dental-oral-craniofacialarena.AnsweringQuestionslike researchquestion centeredonlongitudinal
toothand restorationsurvival inadental practice population
Dentaquest quest is:
“At DentaQuest, we put our "money" where your mouth is. That is to say, our expertise and
resources are focused on delivering you competitive dental benefits programs with high-quality
service.”
Patient Data Of treatment history and his/her noted down stats of hygiene other factors can be
used to create mathematical models of predictive analytics for Both Quantitative and
Qualitative intelligence inputs to patients that can be used to optimize Healthcare Medicaid
and Medicare.
Quantitative Classification based on correlation of Data over Time:
(month 1 + Month 3 + Month 6) = 100% how correlation of this data score over period time.
Kappa Correlation between This Data looks like:
For ( Score 0 { for 3 groups : B,3,6}, Score 1 { for 3 groups : B,3,6} , Score 2 { for 3 groups : B,3,6}]
Qualitative Inputs derived:
In the interpretation of the Kappa statistic,values under
0.00 were considered poor,
0.20 slight,
0.21-0.40 fair,
0.41-0.60 moderate,
0.61-0.80 substantialand
0.81-1.0 almost perfect agreement.
Regression Analysis Data Gathered: (Hypothesis Testing) And Range

2. Oral Cleanliness: oral cleanliness category with Plaque Index (Silness and Loe)
Saliva: Dry mouth clinical evaluation. lips, tongue,gums and tissues categories with the presence of oral lesions
(WHO guidelines).
dentures category with denture assessment (Rise Study).
natural teeth category with tooth status (NIDR)
dental pain/behaviour : category with self-reported pain and a list of problems with oral hygiene care
Chi-square (Test of hypothesis Testing Of Disease probability)
Distance vector based learning To derive individualised outcome to keep
Regression
control chart
Quality characteristic
measurement within one
subgroup
Dependent of process
control variables
Variables
Large (≥
1.5σ)
The Variance Data for under Each Category Can be analysed over time Series Analysis:

3. For inter care Reliability and Intra-care Reliability.
For Buckets of Doctors categorized by same algorithm used for Product categorisation Inter and Intra bucket Kappa
Correlation can be studied relationship For better classification Of Doctor for patients based on (Closest Doctor
geography , Better stats intra kappa, inter Kappa for another bucket pricing time scheduling can also be taken into
consideration along with that.
Person Correlation between Doctor/Tool Assessment and people agreement reflecting faith in derived stats Feed
into algorithm to get more people into systemlet out and Finding Reasons for left out people.
Evaulation Can Continously assist Resident underMedicaid For Oral hygine Giving then Real data and alerts.
Individualized Oral Hygiene Plan, prioritisation based on expected diseased patient data Correlation, dental needs
and interventions needed can be alerted over regular hygiene visits

4. Integration Of Care plans concerning dental, nutritional, mealtime and Swallowing issues overApp.
This Stats Can be shared with government Who are aiding The Medicaid for Efficacy and outcomes for individual
spends.
Strongly correlated Data Can give right assessment while data frequently Changing Can be filtered out From the
model. Fuzzyness Of data between 0 and 2 Can be studied.(data set, Probability of membership)
OUTCOME:
- reduced resident Dental problems:
- improvement or declince of dental problems.
- Interbentions Like Oral Hygiene Care plan.
“DentaQuest, a dental benefits management company, is one of the largest administrators of
government dental benefits programs, in addition to providing a full range of commercial
dental plans.”
Dental Risk Management for indiv idual patient Can Throw light into These areas.
Protective Factors
Brush and floss daily
Annual or semi-annual check ups atdentist
Use of fluoride toothpaste
Sealants applied to molars
Topical fluoride varnish applied
Risk Factors
Poor daily hygiene or no daily hygiene
Irregular visits to dentist
A cavity within the last3 years
Prolonged use ofbottles by babies
A tooth lostto decay or dental disease

5. Communitywater is fluoridated
Use products with xylitol
Good genetics
Puffy or bleeding gums
Receding (shrinking) gums,rootsurface is exposed
Diabetes
Pregnancy
Tobacco use (cigarettes,pipes,cigars,chewing)
Prescription/over-the counter-medicines
Braces or partial dentures
Between meal consumption ofsugaryfoods
Between meal consumption ofacidic foods
Chemotherapyor radiation therapy
Eating disorders
Drug or alcohol abuse
Special health care needs
Dry mouth
Prescription/over-the counter-medicines
Braces or partial dentures
Between meal consumption ofsugaryfoods
Between meal consumption ofacidic foods
Chemotherapyor radiation therapy
Eating disorders
Drug or alcohol abuse
Special health care needs
Dry mouth