Point of View

Single LongitudinalHealth Record (SLHR) to manage patient data

Building on belief

AbstractIn general, patient/citizen healthcare data exists as data sets in multiple data sources. There is no single discrete view with this approach. Single Longitudinal Health Record (SLHR) addresses this with a holistic and longitudinal overview of patient/citizen health. Healthcare providers and governments are targeting SLHR to provide high-quality care delivery at optimal cost. While some care providers are planning to achieve SLHR at a hospital chain level, some nations have it planned at a state level, and others are starting out on foundation level API strategy.

An important aspect to keep in mind with respect to SLHR, is data privacy, and especially in the context of children’s data. There are privacy regula�ons in many countries regarding personal data.

The objec�ves of SLHR are that pa�ents/ci�zens should have the ability to easily discover, navigate, and share their complete health data, and caregivers should be able to access it.

Overall, there are mul�ple data sources and medical data elements as depicted in the figure below.

However, it is important to remember that expansive data history is not necessarily useful because clinicians primarily seek relevant and not exhaus�ve history. Business logic, therefore, needs to pull relevant history apart from that which is �me-bound or �meline-centric. The challenge can be addressed in mul�ple, solu�on-led ways.

Child Vaccina�ons history Child Halth checks Screening results

Historical episodes record Recent and former prescrip�ons Caregivers informa�on

Lab and Radiology tests and results Hospital Clinical documents Structured clinical data Endoscopy reports Cancer history

Personal symptoms monitoring Mobile based ac�vity tracking

Elderly and high risk pa�ents data from Internet of Medical Things

Data ElementsData Sources

Data from Hard copy paper books

Data from wellness devices

Data from IoT monitoring devices

Health records uploaded byPa�ents/Ci�zens

EMR data from Private, Stateand Federal Hospitals

The Centralized / Cloud approachThis involves managing the data at a central loca�on / cloud using a custom-developed solu�on. The data must be stored in a common place; if store in mul�ple loca�ons, it is key to ensure that the data is replicated in the central instance.

Advantages:

Zero duplica�on of pa�ent registra�ons across hospitals and clinics

Control of duplica�on of tests performed for a pa�ent in laboratory and radiology departments

Limita�ons:

Pa�ent/ci�zen health records stored in a centralized database makes it vulnerable to data breaches

Need to consider op�ons such as mirroring or replica�on to avoid single point of failure

If there are mul�ple instances of data, one must consider that this data may not be real-�me; plus, network bandwidth is key

A poten�al high wait �me for pa�ent discharges

Applicability:

Not prac�cal for regions / na�ons where work is already in place in terms of SLHR

Not a prac�cal solu�on for large scale use

Region N

Regi

on 1

Regi

on 2

Hospital N

WAN

WAN

WAN

WAN

WAN WAN

Hospital 1

Main Hospital

Hospital NCentralized Data

Instance

WAN

WAN

WAN Hospital 1

Main Hospital

Main Hospital Hospital 1 Hospital N

The decentralized approach with a hub-and-spoke modelIn this scenario, pa�ent data is stored locally in regional instances. A central instance is maintained to store relevant data (demographic and clinical) belonging to all-regional instances. Data from regional instances is replicated during off-peak �mes within the central instance. Clinicians can pull the data from the central instance to get a single view if the pa�ent moves to another instance from the current, where the data originally belongs. Consolidated data at the central instance, is also used to generate enterprise-wide MIS reports.

Advantages:

Reduced turnaround �mes for key processes: Pa�ent discharge, pa�ent registra�on, diagnos�c report genera�on and availability across hospital loca�ons

Quick pa�ent diagnosis with access to tests (laboratory, radiology) done in another city/region

Limita�ons:

Certain data may not be real-�me; again, network bandwidth is key

Applicability:

Applicable at small scale, not a prac�cal solu�on at a larger scale

Not applicable for cases where network bandwidth is limited or expensive

WAN

WAN

WAN

LANWAN

WAN

WAN WAN

Centralized DataInstance

De-CentralizedInstance

De-CentralizedInstance

De-CentralizedInstance

Hospital 1

Main Hospital

Hospital N

Hospital N

WAN

WAN

WAN

Hospital 1

Main Hospital

Hospital N

Main Hospital

Enterprise MIS

Enterprise Pa�ent Movement

Enterprise Clinician Movement

Enterprise Process Standardiza�on

Enabling

Region 1 Region 2

Region N

The HIE approachThis approach involves storing the data as-is. The pa�ent ID/ Unique Health Iden�fier is used to correlate records across data storages. The Health Informa�on Exchange (HIE) pla�orm with Fast Health Interoperability Resources (FHIR) is used to populate the longitudinal health record. Op�mize the search func�onality with the appropriate design pa�erns.

Advantages:

Minimal changes to the exis�ng setup

Proven solu�on and technology

Has been adopted by many na�ons

Limita�ons:

Need to build a unique health iden�fier, if it isn’t present in the as-is setup

Need to fetch data from mul�ple data sources

Applicability:

Most suitable for cases, even though work is done towards SLHR

Presentation Layer (Single Longitudinal

Record Clinical Portal/ Mobile App)

Data Sources Layer

Data from Hardcopy paper books

Health records uploaded by Patients/Citizens

Data from Wellness devices

Data from IoT monitoring devices

EMR data from Private, State and Federal

Hospitals

Integration Services

Layer

Façade Pattern: Data collation from multiple data sources

Health Information

Exchange (HIE) Engine

Fast Healthcare Interoperability Resources(FHIR)

Engine

Health Level7(HL7) Interface

Engine

Data Elements

Child Vaccinations historyScreening results

Historical episodes recordsRecent and former prescriptions

Clinical DocumentsInvestigation Results

Elderly and high-risk patientsEntitlement Records

Lab and Radiology tests and resultsHospital Clinical docsStructured clinical dataEndoscopy reportsCancer history

Users:

API

Clinical Staff

Functional CapabilitiesUser AuthenticationPatient SearchView Patient RecordRelevant Episodes History

Non-Functional Capabilities

Audit trial of Read, Write and Update recordsSys admin functionsTools to audit patient record access

The blockchain approachThis is a con�nua�on of the decentralized approach. It is important that one not store medical records in the blockchain (off-chain storage of records). It is also important to store reference to where the data is currently present, in addi�on to storing references to the type of info, and when the visit is made in the blockchain. The approach uses FHIR and share the data with pa�ent consent. Each user has an updated copy of the blockchain; thus, hackers cannot obtain control over the ledger and hold it ransom.

Advantages:

A decentralized ledger in EHRs, means data cannot be held ransom

Mul�-level permissioned access to Electronic Health Records

Secure sharing of pa�ent informa�on with consent to ensure regulatory compliances

Ci�zen/Pa�ent stays custodian of their medical records

Limita�ons:

Lack of universally defined standards

Giving that the technology is evolving, there could be unknowns

Shortage of skilled IT specialists who can understand both the healthcare domain and blockchain

Applicability:

First requires a prototype proving proof of concept

Applicable for those na�ons / regions where limited work has been done so far on SLHR

While each of the solu�ons outlined above has its advantages, limita�ons and is applicable only within certain contexts, the blockchain solu�on, par�cularly, is at a conceptual level and yet to be proven. The centralized approach and HIE approaches are most suitable in major contexts.

Patient-My Health Records

PatientSLHR View

RestrictedView

Hyperledger Fabric or Hyperledger Sawtooth Permissioned Blockchain

Payer 1Node

Department of Public Health

Node

Hospital NNode

Hospital 2Node

Node Data Synk Encrypted

Pharmacy

Wellness Provider

Clinic

Elder Care

Remote patient Monitoring

Hospital 1NodeAPI

Interplanetary File System

IPFS

Corp

orat

e M

arke

ting

| De

sign

Serv

ices

| M

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