1

Advancement in healthcare

Technology

An Analytic Modelling in Healthcare Industry :

Current Status !!

“The new wave “

DR DEEPAK YADUVANSHI Ph.DMBBS.MD.DNB.FCCM.MHA

Head & Chief Consultant

Department of Respiratory Medicine

Manipal Hospitals

acknowledgment!

Teachers & well

wisher’s

Family

2

3

Key Question ?Objective…Cochrane review !

Hospital is good

Hospital is bad

4

Patient’s are changing & demanding so is

Technology …

Bigger change to patient DNA in <5--10

years than past 2000 years

< healthcare +Technology = advancement >

6

Presentation Plan….!

• Augmented Reality

• Patient Access solution

• In silico Clinical Trials

• Genomics ……”designer baby “

Medical Education

• Biosensors

• Wearable watches …..devices

• CPAS ….Clnical Pt access solutions !!!

• PPP……Patient provider Payor analytics

Internet of Things (IoT)

• Heart In a Box

• Artificial Lung , kidney

• Targeted Immunotherapy

• 3D Printed Drugs +Implants Clinical

7

AR / VR

8

3D Printing …drugs /implants

9

Internet of things

IoT

10

I-oT

11

4PPersonalized

Predictive

Participatory

Public

EHR

IoT

Biosensor

Insulin delivery

Non Invasive Monitoring

5G e-ICU

Artificial Organs

Block chain

Healthcare payor

Providor

3D Printed Drugs

12

Impression……….

Information Overload

Routine noninvasive monitoring

• EKG

• Arterial blood pressure

• Heart rate

• Respiratory rate

• Temperature

Fluid balance

• Fluid IN

• Fluid OUT

• Urine output

Laboratory blood

• Hemoglobin

• Serum electrolytes

• Blood chemistry

Invasive hemodynamic monitoring

• Central venous pressure

• Arterial blood gases and pH

• Pulmonary arterial pressure

• Oxygen transport variables

• Intra-arterial blood pressure

Natural contexts

• Demographic data

• Chronic diseases history

• Allergies

• Stress

• Pain

Tissue perfusion / oxygenation monitoring

• Pulse oximetry

• Transcutaneous oxygen and carbon dioxide monitoring

Ventilator monitoring

• FiO2

• PIP

• PEEP/CPAP

• Mean Airway Pressure

• Tidal Volume

Brain function monitoring

• Electroencephalography

• Intracranial pressure

Routine cardiac monitoring

• Cardiac output

• Hemodynamic variables

• Blood volume

• Colloidal osmotic pressure

Average data points per day

Per Patient Per 24 bedded ICU

Labs 60 1440

Drug Orders 10 240

Microbiology 2 48

X ray 2 48

Vitals 1950 46800

15

Data sources

•Ventilator devices

•Bedside devices

•Medication monitoring

•Online monitoring

•Blood work

•X-rays

•Nurse’s station &

patient records

•Doctor’s records

•Physical therapy

•Well-Being sheets

•

Hospital Matrixes

nursing indicators

Needle Stick Injury

Patient Incidents (Falls)

Patient Incidents (Pressure Ulcers)

Potential Medication Error

Average time to start treatment at ward / ICU/ HDU

Actual Medication Error

MRD

Completion of History & Physical Sheet within 24 hours of admission

Allergy documentation on drug chart

% of Death Notification (in 7 days)

OPD

Total No. of New Consults

Revisits - within 7 days

Percentage of Drs. arriving beyond scheduled OPD time

Average waiting Time for Consultation

Average delay in start of Ist consult

Average queing time for Billing

Patients delayed >15 mts. post appointment

Average waiting time for appointment

Appointments cancelled

No. of Refunds due to Errors

cath

Procedures for the week

Number of day-care procedures

No of daycare procedures delayed beyond the day

% of elective proceudres scheduled

Cath lab Utilisation (8 am to 8 PM )

Primary PTCA (Door to balloon time)

Reports signed by Consultants se day

Financial counselling &billing

% age of step downs planned

%age of discharges planned

Discharges with billing estimate off by >5% (Surgical cases)

Patients having cash positive balance at the time of discharge

Billing time for discharge (Discharge intimation to bill handed over to attendant)

Bill clearance time (Bill handed over to attendant to bill clearance)

IPD Admissions met by Counsellor

HRAttrition Rate

Nursing staff Utilisation

Avg per bed BMW in kgs

Medical Data Mining

P

P

P

17

Medical Data Mining spectrum

18

Q - Index….for hospital

19

Hospital Data Mining <PhD.. 2012-17>

Topic

• Pre PhD Course work : Conceptual Framework 10/2012

• Stage I Research design and methodology 03/2013

• Research Topic Finalized 06/2013

Data

• Sample Work commenced 06/2014

• AS –IS –SOP and baseline parameters 11/2014

• Data Collection 1/11/2014 – 31/07/2015

Analysis

• Preliminary Analysis 10/2015

• Data Interpretation and compilation 12 /2015

• Research committee revision suggestion for Data Analysis in May 2016

• Revised & submission May /2017

• Lean Hospital Index …(LHI )

20

Score card “18c Manipal way ……

21

Medical Data Mining ( MDM)

22

Marriage with Healthcare Technology

What

When •

Way • ??

23

4PPersonalized

Predictive

Participatory

Public

EHR

IoT

Biosensor

Insulin delivery

Non Invasive Monitoring

5G e-ICU

Artificial Organs

Block chain

Healthcare payor

Providor

3D Printed Drugs

24

25

26

27

BlockChain : Healthcare Ecosystem

28

Healthcare +tech=Holy alliance

29

Challenges !

30

Diagnostics…..POC

31

Technology ….bliss or curse!

32

AI

33

Practical Implications Technology

A: Hospital Managers & Administrators

❑ a)Quality assurance

LHI : Vital Instrument to assess and measure the goodness of HMS , As

has clinical Processes and Non clinical Processes

❑ b) Hospital Administration

It helps for early identification of selected quality problems of different

units in a health organization and accreditation pathway

B:Academicians & Researchers :

❑ Data Analytics:Predictive Model

▪ The opportunity for Data mining huge as model developed encompasses hospital

operation systems as whole

▪ Data analytics is a predictive model for a healthy hospital in terms of not only financial

health but also structurally .

▪ Big Data of admissions , diagnosis , outcomes , services and clinical outcomes .34

?Health care tech…

35

Healthcare Industry…indices 3 level

36

Adapted from Robert S. Kaplan and David P. Norton, “Using the

Balanced Scorecard as a Strategic Management System,” Harvard

Business Review (January-February 1996): 76.

37

38

Practical Applications

Bench to bedside

Huge unmet need efficient hospital operations analysis

Cost advantage

Opportunity to innovate

Competition for operational efficiency …focus on Processes

Transparency

Operational Efficiency

39

41

• Medical Education

42