Algorithms in #EMR by Dr. Joyoti Goswami




Practicing physicians these days are barraged with a lot of technical jargon promoted by the Information technology professionals such as Big Data, Hadoop, Artificial Intelligence and Predictive analytics. For a physician not introduced to the these terms, the conversation is of little value unless there is a specified value in the clinical setting.

Pulitzer Prize winning author Siddhartha Mukherjee in his book ‘Emperor of Maladies’ has quoted “The greatest clinicians who I know seem to have a sixth sense for biases. They understand, almost instinctively, when prior bits of scattered knowledge apply to their patients—but, more important, when they don’t apply to their patients.”

So the accumulation of all the 6th sense of multiple physicians in the form of scattered notes in documents is the food for Big Data professionals, who curate this data manually with standard vocabularies and then analyse patterns that can help physicians make informed decisions at the point of care.

While Predictive Analytics is a huge winner if there is genomic data available, the fact remains that in current clinical scenarios, complete genomic sequencing is not yet the norm. They are still restricted to the areas of research as the cost of doing a complete sequencing of the genome is still about $ 1000 and very few institutions are prescribing it as a norm in routine clinical practice. Once that is in place, it can lead to a whole lot of practical applications and use cases making the use of technology like Big Data and Hadoop. 

Till then, use cases which use traditional computing powers like SQL and simple queries and algorithms running on top of it could be used to get the benefits of clean data and technology in the clinical workplace.

In an attempt to utilize Predictive Analytics and Artificial Intelligence in the world of EMR and healthcare data, a list of 5 practical clinical use cases and 5 administrative/Claims Related use cases of how data could be used at the point of care and integrated with the EMR is listed out below:

Clinical Usecases

1 Acute Kidney Injury

According to Statistics, 1.2 million people per year get AKI during a hospital stay and 300,000 people in the US die annually due to AKI, this is more than breast cancer, prostate cancer, heart failure and diabetes combined. 

The successful documentation and implementation of the AKI algorithm published by the NHS is a systematic and step by step program that can help reduce the incidence of Acute Kidney Injury and the mortality and comorbidities associated with it. The e-alert for AKI installed in the LIMS (Laboratory Information Management Systems) alerts physicians when a patients’ Serum Creatinine rises greater than 26 mmol/litre from a baseline within 48 hours or there is a rise of 50% or more in 7days and/or the urine output is < 0.5 ml/kg body weight/hour. https://www.england.nhs.uk/wp-content/uploads/2014/06/psa-aki-alg.pdf

2 Sepsis Management

Sepsis or SIRS (Systemic Inflammatory Response Syndrome) accounts for 20 to 30% hospital deaths and $15.4 billion in annual healthcare costs. Needless to say early diagnosis and treatment is critical to optimal care. 

In a study, it was found that 24% of infected patients with 2 or 3 qSOFA (or Quick Sepsis Related Organ Dysfunction Assessment) points accounted for 70% of the deaths. An automated EMR based Sepsis identification system helps to detect cases with sepsis. A sepsis sniffer algorithm in ED identifies patients who exhibit at least 2 of the 3 criteria of Altered Mental Status, Fast Respiratory Rate and Low Blood Pressure and initiates the Sepsis Order set if the results of the score warrant it. The content on the Sepsis Order Set helps to make sure that none of the key variables for the management of sepsis is missed out.
https://www.mdanderson.org/documents/for-physicians/algorithms/clinical-management/clin-management-sepsis-management-adult-web-algorithm.pdf

3 Initiation of Statins

Mayo clinic has implemented a single click decision support tool within the EMR to automate the calculation of 10-year atherosclerotic   cardiovascular risk and populate the statin choice decision.  Based on the risk score calculation parameters of the ASCVD (atherosclerotic cardiovascular disease), the tool populates whether the patient needs a statin or not and if yes, which statin would be the best choice. This is defined on the basis of an algorithm running which has historical data of similar patients along with their outcomes.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157431/

4 Algorithm to identify major cardiac adverse events while on statins

Cardiovascular disease is a leading cause of death worldwide and statins are largely prescribed for the same. A study was conducted using historical data to identify patients with MACE (Major Adverse Cardiac Events) while on statins for primary prevention.  This algorithm achieved a 90 to 97% positive predictive value for the identification of adverse cardiac events while on statins
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333709/

5 Detection of Diabetic Retinopathy

Diabetic retinopathy is the fastest growing cause of blindness and screening patients with diabetes for retinopathy has been mandated as part of all the Quality Measures such as MIPS, HEDIS and others. Google analysed a dataset of  128,000 images of the retinal fundus and used that to predict retinopathy based on the lesions present on the scan such as microaneurysms, haemorrhages, exudates etc. A blindfold comparison of the results predicted by the AI algorithm was compared with the results given by experienced ophthalmologists. The AI algorithm had an accuracy of 90% which is a huge win especially in areas where there is a shortfall of specialists to interpret the results.

In the areas of diagnostics especially interpretation of Imaging results, a lot of good work is going on and soon AI capabilities will lead decision making in the interpretation of reports.
https://ai.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html

Physicians working closely with Data scientists can help create some exciting algorithms given that healthcare centres today have large volumes of data sitting in their EMRs. A physician with even 50 patient records of 2 years has sufficient information to derive useful insights on predicting interesting patterns on the future health and preventive measures therefore can be planned accordingly. The approach for healthcare institutions should be to first identify their most pressing problems and then evaluate if any kind of Prediction of solutions to those problems could lead to better outcomes in patient care. A well thought and organized approach could go a long way to develop algorithms that work well for the hospitals and ambulatory clinics to achieve their goals and have favourable outcomes for both the patient and the hospital.

Administrative Use Cases:

In Patient Registration systems and billing systems, the algorithms can have a different flavour, some of which are listed below:

1. Healthcare Utilization Management: Integration of hospital resource utilizations in different departments, risk management and quality assurance into a management dashboard in order to ensure the judicious use of the facility's resources. A review of the procedures and services rendered by the hospital and the resources used by them (such as rooms, timings, instruments, admin and clinical staff) can help to identify under utilized and spare capacity of the hospital. This can be inbuilt in the system with the help of algorithms so as to get an optimal effect.

2. Capturing Missed and Incorrect Charges: Many facilities lose revenue due to billing errors done manually. Billing amounts associated with each DRG (Diagnosis Related Group) can be used to create models and compare them with the actual billing. Outliers in patient invoices can help to identify the likelihood of missing or incorrect charges. The higher prioritized invoices can then be reviewed to confirm the charges, which may be incorrect, over billed or under billed.

3. Predicting denials: There is a constant tug of war between the providers and payers about the claims to be settled. Most of the denials are due to incorrect documentation or missing information, duplicate claims, service already paid as a part of bundled services or others, services not covered by payer and late submission of claims. All of the above can easily be avoided by streamlining coding and billing processes. The industry benchmark for medical billing denials is 2% and in practices between 5% to 10%. Reworking on denials has a cost associated, so having algorithms running within the systems can help to streamline these efforts.

4. Predicting wait times and No shows: Idle time in clinics, both for patients and providers can be frustrating. In departments where procedures are done, it is possible to predict treatment durations by looking at historical data. Similarly the number of no shows of scheduled appointments can be predicted by including variables like age of patient, severity of disease, previous appointment regularity of the patient and others. This could help the facility to take in additional patients on the free slots proactively.

5. Measuring Bad debt, Days in A/R and DNFB (Days in Total Discharged Not Final Billed): These metrics help to determine the effectiveness of the claims generation process and effectiveness of collection efforts.

A number of KPIs tracked by hospitals and payer systems can be in built within the Revenue Management Systems and Scheduling systems. These include patient costs, ROI of the facility, Average Patient Wait times, Average patient appointment scheduling times and Patient Satisfaction.

With the healthcare market growing and many EMRs, both locally created and branded companies in the play, it is eventually the content within the EMR that can make it user friendly. Many content companies offer excellent standardised algorithms and content that can be integrated within the EMR, but there always remains physician preferences based on specialty, region of practice and other personal preferences. The end user i.e. the physician ultimately needs to decide on what suits his practice and then have the algorithms accordingly tailored within the EMR.

Author
Dr. Joyoti Goswami
Healthcare expertise of over 20 + years. Clinician having worked in specialty hospitals as medical officer and currently in the Healthcare Information Technology domain since the last 10 + years. Have a good combination of clinical and technology skills in multiple areas. Worked with multiple EMRs such as Allscripts, GE Centricity, Athena health and Nextgen
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#RPA in Healthcare: The Path Ahead for Health IT Leaders By Sreejith Madhavan



Historically, healthcare industry has shown a reluctance to invest in technologies that did not come under the purview of diagnostics and treatment, or demanded by insurance payors (such as electronic claims submission). Anything that required cognitive (human) intervention or intuition was kept aside from the technological takeover. The unprecedented growth of life expectancy, the discovery of new drugs and treatments, and the ability of modern medicine to combat chronic ailments and epidemics have spurred the need for technological inclusion in multiple areas of healthcare.

As patients become more digital savvy, caregivers are increasingly implementing technology solutions that enable both parties to perform several activities online such as accessing personal medical information to online scheduling of appointments. Today, healthcare industry is looking at those technologies or combinations of technologies that can optimize their front, middle and back-office operations so that care givers get adequate time to spend on priority tasks.

Robotic Process Automation (RPA) is one of the key technologies that has gone mainstream in many industries including healthcare. Why health IT leaders should continue to turn their pivot towards RPA? We’re exploring the reasons through this post.

RPA in Healthcare: Common Applications and Benefits

Robotic Process Automation or RPA automates processes that are repetitive and transactional, primarily by imitating human behavior for rule-based tasks.  RPA enables caregivers to focus on high-value activities by enhancing overall administration of healthcare processesIt executes routine tasks at a fraction of time than that’s taken by a human, eliminating the risk of human errors. The scope of RPA in the administrative and clinical functions of healthcare is very vast. 

Technologies such as cloud computing and data virtualization have enabled scalable deployment of RPA software across various units and geographic locations of a healthcare organization. So far, healthcare administrators have leveraged RPA in several areas of their back, middle and front-office operations; few of which are mentioned in the table below:  


Healthcare

Areas of RPA implementation
Benefits to healthcare providers
Back Office


  • Human resource management
  • Finance and supply chain management
  • Streamline onboarding process to improve efficiency
  • Clinicians can impart care without interruption caused by administrative functions
  • Human resource management
  • Ensure new clinical staff gains access to systems and facilities from day 1
Middle Office


  • Revenue cycle management
  • Claim submission and reconciliation
  • Patient scheduling
  • Accelerate revenue cycle by automating coverage eligibility verification process, claims posting, and claim resubmission
  • Insurance data management
Front Office

(relatively untapped by RPA)


  • Care delivery setting
  • Health data utilization and report generation
  • Integration of disparate care management systems to assimilate date efficiently
  • Ensure clinicians spend more time for patient care by minimizing their administrative work
  • Enhance case management

Most of the present day healthcare organizations are using RPA for automating rules-driven and repetitive back office work. The potential RPA can offer healthcare in unison with advanced technologies such as machine learning (ML) and artificial intelligence (AI) is tremendous. It’s no surprise if we consider Robotic Process Automation a stepping stone to integrating these sophisticated cognitive technologies into healthcare.

What needs to be automated in healthcare?

Here’re a few potential use cases: 

1 Connecting and automating disparate health monitoring devices: The case of neonatal ICU:

A 2017 Business Insider post talks about the need to automate oxygen supply to patients hospitalized with pulmonary hypertension. Currently, the system only alerts the staff (nurse) through a monitor beep when the blood oxygen level of the patient drops and the staff has to attend the case. If the nurse is attending other patients and misses out the alert, the chance for a mishap is more. The article from Thomas Hooven, a Neonatologist in the U.S. suggests how automation of oxygen inflow at the moment of crisis could save patients with chronic pulmonary hypertension.

2 Compliance monitoring and analysis:

Imagine a hospital that processes thousands of claims daily and attends the need of a large number of insurance beneficiaries. RPA can be used to gather and consolidate data from multiple disparate sources or systems that improves the efficiency of regulatory, non-financial, and risk reporting. Automation of compliance monitoring analytics eliminates time-consuming activities involved in the collection, compilation, cleansing and summarization of large amounts of information. Security of medical data and records is a major concern for any healthcare organization. Robotic Process Automation helps protect patient privacy and achieve compliance with HIPAA and other mandatory health regulations by generating custom reports and detailed audit logs.

3 IoT analytics to empower process automation

The goal of any IoT deployment should not be limited to collecting data from multiple sources (devices). It must ensure that the data is actionable in real-time, to support relevant processes. Process automation is recognized as the common endeavor to improve operational efficiency by lowering costs, increasing profits and improving customer satisfaction. Integrating IoT into process automation could deliver greater value across product lines. For instance, consider the claims settlement process in healthcare that is deeply influenced by the data being collected from several devices. During the claims settlement process, if the system could take into account the details of the data aggregated by IoT devices such as lowering a premium based on usage behavior, or a difference in user-provided information, that could lead to process optimization and faster decision-making. IoT analytics in healthcare can avoid the cost of admissions by automating prescriptions, reduce medical error in treatment and improve quality of patient services.

Leveraging RPA with exponential technologies

RPA is just one of the growing technologies that can empower healthcare organizations. Once RPA is integrated successfully into their core business strategies, hospitals should consider incorporating the advanced spectrum of cognitive technologies such as AI and machine learning. Unlike RPA, artificial intelligence has the ability to identify patterns in data. Similarly, machine learning adds more meaning and power to process automation by enabling healthcare organizations to identify payment variance and remediate complex payment methodologies.

The future healthcare environment could look very different from what we see today. Technologies like Robotic Process Automation will have a greater say on employee productivity. Automating routine tasks such as collecting blood samples could help the job of a nurse, reduce task time and eliminate manual errors, while improving the patient experience. As organizations progress from depending on manual tasks to applying RPA and cognitive computing, the workforce also shifts from being “doers” to “reviewers.” Health IT leaders and providers, hence should focus on developing proactive, winning strategies to attain long-term financial sustainability and improved patient experience.

Author
Sreejith Madhavan
Sreejith Madhavan is the Chief Operating Officer of Zerone Consulting Pvt. Ltd., a custom software development company with an exceptional track record of successfully completing over 500 challenging projects for 140 plus satisfied customers globally. Sreejith’s experience includes a demonstrated history of working in the outsourcing/offshoring industry, managing and mentoring multiple teams in the web and mobile development arena
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#mHealth: A New Growth Engine in MedTech Industry by Ananya Bhandari


Source: Grand View Research
Mobile health highlights the risks and opportunities of pharmaceutical and Medtech industries. Surge in number of purely digital players transformed the mHealth app market

According to the estimates of Grand View Research, the global mHealth market size was valued at USD 4.75 billion in 2014 and is expected to witness substantial gains throughout the forecast period. Improvement in 3G & 4G networks and favorable government initiatives in healthcare IT owing to increase in demand for such services. According to WHO Global Observatory for eHealth, about 58% of the health authorities around the globe is involved in the development and adoption of mHealth in health sector. High penetration of smartphones coupled with technological advancement in smartphone applications is also anticipated to further boost up the demand for mHealth services.

Furthermore, the growing adoption of mobile applications paved the way for driving up the demand for various mHealth apps that can be used for fitness tracking, diagnostic & monitoring, consultation, medical information & education services, chronic care management, and ageing solutions. Growing demand for mHealth technology to provide remote patient monitoring services and to run surveillance programs in developing countries, which is further anticipated to propel the market growth. However, lack of reimbursement policies, poor network coverage especially in emerging economies, and data security issues can hamper the growth of mHealth market.


U.S. mHealth Market Revenue by Services, 2014 - 2025 (USD Million)
Source: Grand View Research
mHealth: Market Segmentation

Based on the services, the market is segmented as monitoring services, diagnosis services, healthcare system strengthening, and other services. Monitoring services accounted for major share of over 65% in 2016 pertaining to the factors such as increasing ageing population and rising incidence of chronic diseases such as obesity & diabetes, congestive heart failure, cancer. mHealth helps in monitoring of various health parameters such as cholesterol levels, blood pressure, heart rate, and nutrient intake.

There is continuous advancements in diagnostic device technology that integrates digital technology in medical devices. It enables patients to send clinical data to the healthcare providers through their smartphones. Healthcare system strengthening services provide healthcare surveillance and administration, emergency response and support to healthcare providers. Other services consist of prevention and wellness of patients through mHealth applications. It focused on elderly care, drug abuse prevention, child and women care, smoking de-addiction and healthy living.

Mobile operators held the largest market share of about 49% in 2016. MNO’s provide 3G and 4G broadband network coverage. mHealth allows healthcare professionals to handle appointments and to monitor remote patients. Device vendors are involved in integrating digital technology in medical devices that allows transmission of clinical data such as blood pressure, heart rate, blood-glucose levels and others. Leading vendors such as Biotrick, Medtronic, ResMed and Philips Respironics are successfully using this technology.

Some of the key factors attributing to the market growth include rising number of health and wellness apps with growing adoption of smartphones by population. Asia pacific region is expected to be the fastest growing segment with a CAGR of 27.2% over the forecast period. 
Source: Grand View Research

The growth in the region is driven by the factors such increased healthcare awareness, improving network infrastructure, rising rural population and government initiatives towards digitalization in countries like India and China.


In addition, improving internet connectivity, reduction in costs and increasing use of mHealth for various surveillance and awareness programs for rural areas drive the growth of mHealth in the region. Growing number of surveillance programs for AIDS & other infectious disease and rising incidence of chronic diseases in Latin America and Middle East Asia drives the demand for mHealth applications.

Author
Ananya Bhandari
Ananya is a MBA in Marketing with a professional experience of nearly three years in healthcare domain. She is a Research Analyst, medical devices and healthcare IT, at Grand View Research. Ananya has been working with various Fortune 500 companies in medical devices and healthcare IT domain. She is well versed with healthcare market trends and specializes in strategy building and competition insights. She has successfully driven a team and delivered over 100 market research, due diligence, and consulting assignments for various industry participants and management consultants.
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What does it take to build real-world #AI enabled healthcare solution? By Vijayananda J, @vijayanandaj



Development of new technologies has undoubtedly enabled several breakthroughs in the healthcare industry. To put it simply, it has revolutionised the growth of healthcare from nascent patient-care to accomplishing treatment of life-threatening diseases. High-performance computing and the availability of digital data have extended these remarkable outcomes explaining why AI-based healthcare solutions are at top of the funding lists and are continuously gaining traction.

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NITI Aayog’s National Health Stack - a Healthy Stack?! by Divya Raj @divyaraj1




Extraordinary problems need extraordinary solutions. And creating a country level IT infrastructure addressing challenges in India's Healthcare management for its 1.3 billion population definitely falls very well into that category. 

NITI Aayog's “National Health Stack - Strategy and Approach” document published in July ’18 is a good starting point in the direction of digitizing India's healthcare management for meeting the challenge of healthcare of India's masses. It’s a clear reflection of the realization that India’s Healthcare needs a digital infrastructure. The National Health Stack (NHS) is outlined as a "visionary digital framework" with four key components -- electronic health registries of health service providers and beneficiaries, a coverage and claims platform, a federated personal health records framework and a national health analytics platform. 


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#Blockchain in Healthcare: Will it or won't it survive? By Tirupathi Karthik, @TirupathiKarthi




What is Blockchain

Blockchain offers a permanent record of online transactions. Transactions are deemed as a “Block” and a ledger binds them in a “chain” thus earning its moniker “Blockchain”. Each transaction is validated and stored by a network participant based on rules but sans a governing central authority. Information can neither be modified nor copied or deleted.

Every transaction has a time and date stamp, offering a trusted transaction history and allowing verification of such records. Since the information is encrypted, the only way to access the blockchain is with a passcode. This shared ledger system makes Blockchain rather secure. Given this, Blockchain is gaining new use cases for applications that require trusted and immutable data.

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Containing Health Care Cost, What is our role as a Physician? by Dr. Chandrika Kambam @Ckambam




Indian health care is at an inflection point. Today governments’ spending on healthcare needs is one of the lowest amongst the Developing countries [1]. India spends about 5% of the total expenditure on Health which is around 1.7% of the GDP. Public healthcare growth has slowed down over years. In 1998 about 43% of population was served by Public Hospitals and today only 30% use the Public health care system. [2] That means almost 70% of the health care needs are serviced by Private players, trust hospitals and non-profit institutions. This has led to the rapid growth of Private players who are growing at the rate of CAGR 16.5% year on year [3]. The costs of procedures or hospitalization has increased anywhere from 83% to 263% in 10 yrs. i.e. 2004 to 2014. There is also a wide variation of the cost for the same procedure in different hospitals [4]. It is also noted that 86% of rural Indian patients and 82% of urban Indian patients do not have access to any form of employer-provided or state-funded insurance.

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