Mobile app data ussage: use of health apps in health management

1. Use of mobile apps in public
health and management – The
first data for drGrouper app
usage
Dr. Mihai Negrea, Dr. Iulia Grancea

2. Why?
• Today we know that internet and technology get in every aspect of
our life personal and professional
• Why not make our jobs easier with technology?
• If we can offer good products and make our health better why
shouldn`t be doing it?
• Romanian health system data is closed for outside researchers,
developers, entrepreneurs, only a hand few of people have acces less
than 10
• We plan to give public acces to our data

3. What`s DRG
• Diagnosis Related Groups
• It`s a financing system used in Europe, USA and some countries of
Asia(China, Korea, UAE, etc) Australia
• It`s based on the fact that similar pathology are using the similar
resources so they need to paied the same.

4. How
• We created at Reea the app available on mobile devices – iOS and
Android systems
• It can be only used by registration
• It`s free to download to smartphones or tablet
• We ask information about user – name, email, profession,
specialization or ward, hospital, city, county
• When use the grouper we can associate the patient cases – diagnosis,
procedures, sex, age, weight, length of stay and discharge and the
result of the grouper

5. What do we have at the moment
• ~310 active users
• 2600+ grouped cases
• 1436 Medical cases
• 648 Surgical Cases
• 22 Others Cases
• 1.0997 – Case Mix in app

6. So what I did?
• I took the 3 of the most frequent medical groups in DRG report for
2015
• And associated the group with my database
• So I know what is the primary diagnosis, what are the most frequent
associate pathologies
• what`s the average age, length of stay
• What`s type of the outcome – discharge, transfer, death

7. F3032 – Heart failure and shock without
catastrophic complications
• 32 cases - 22 males – 10 females
• 65.62% i50.0 – congestive hearth failure, 25% i11.0 – hypertension
cardiopathy with congestive failure , 9.38% i50.1 – Left Ventricle
Failure
• Associated diagnosis i10 – Blood Hypertension 40.62%
• N18.x and n17.x– renal disfunction 21.87%
• Average length of stay 5.78 Days min 4 days max 12 days
• All cases discharged

8. I3081 – non surgical perturbation of the spine
• 5 cases all males
• Main diagnosis 3 of m54.4 Lumbago with sciatics and 2 m47.xx
spondylosis
• Every case has associated at least a form of gastic affection k76.x,
k30.x, k21.x,k62.x
• 4 out of 5 have at least 1 urinal affection and hypercolestrolemia
• Average length of stay 5.2 day , minim 2 max 9
• All cases discharged

9. E3061
• 8 cases – all males
• Main diagnosis j44.x – a form of Chronic Obstructive Lung Disease
• Associated diagnosis – 6 acute respiratory insufficiency
• 4 have a form of pneumonia
• 4 have heart or circulatory conditions also
• 2 have diabeties

10. Discussions
• Of course these data are not relevant to take medical actions at the
moment we have 4.2 million patients admitted every year
• Of course they could be better collected but we`re still developing the app
and the data management system
• But it`s the first in the Romania`s history when these datas are publicly
available – anyone can ask for them we`ll give them we present them
• There are many software proivders that have these datas but they don`t
make them available for research and also don`t public statistics
• Also the Romanian School of Public Health and National Insurance Service
has them but they are not publicly available for research

11. Conclusion
• We can say we have some data but we need many more
• We can say we can model some patterns of public health indicators
• There are some pathological patterns that we can analyse further
• We consider we have an alpha model, proof of concept that we can
develop further

12. Futher development
• Better data collection
• Better data organizing and data mining system
• Attract more active users I estimate that in Romania are around 10
000 MDs – whole market

13. Thank You