5 Key Non-Traditional Areas of Cost Optimization in Healthcare

A recent analysis from the Office of the Actuary at CMS, reports that the national healthcare spending reached a total of USD 3.8 trillion before the pandemic hit the world. Conversely, there has been a steady decline of around 20% and 34% in inpatient and outpatient volumes, respectively. As a result, the American Hospital Association (AHA) predicts that the higher-than-usual supply expenses and other pandemic-related costs can lead to USD 323 billion loss for hospitals and health systems across the country. To curb the mounting expenditures in the healthcare industry, healthcare CXOs are shifting focus to cost optimization strategies. However, before focusing on cost optimization, one must consider:

  • Evaluating the role of IT in improving patient care
  • Prioritizing technology initiatives to address medical costs
  • Being prepared for internal backlash on account of budget cuts

Here are five non-traditional areas that can be optimized to bring down healthcare costs.

Medical Waste Management and IoMT

Medical waste in hospitals could have a severe impact. The economic costs associated with medical waste range from USD 760 billion to USD 935 billion, as improper management invites increased premium and out-of-pocket medical expenses, thus increasing healthcare costs. Investing in care coordinators, community health workers, and social workers can help to improve quality and cut costs. Waste management monitoring through sensors and IoMT can help hospitals become more efficient and remain compliant with various regulatory bodies such as OSHA.

Care Management and Process Optimization

In a dynamic environment, a lack of process standardization can increase the chances of variations in patient outcomes, resulting in longer LOS and increased costs to the healthcare system. Leveraging the power of data and data-driven processes, healthcare providers can standardize processes and identify areas that require improvement in terms of care delivery. Over a period of time, the data can help implement evidence-based workflows that improve consistency and coordination across processes.

Diagnostic Testing and Predictive Analytics

A report states that the costs of diagnostic testing account for more than 10% of all healthcare costs. However, there is also a rise in death or disability, with more than a million a year harmed by diagnostic errors, in the US. The repercussions of wrongful diagnosis can insure both direct and indirect costs that include medicolegal costs, increase in medical liability premiums, among others. While there is still an ongoing debate to determine whether diagnostic tests are being overused or underused, the cost factor of unnecessary testing must be taken into consideration. With the growing use of technology in healthcare, hospitals and other medical facilities must leverage electronic health records (EHR) for information transfer across care teams. The data acquired from EHR can be used in combination with new-age technologies such as predictive analytics and machine learning to minimize human errors and costly adverse events.

Data-driven Decision Making

The World Bank reported that the US leads the world in healthcare spending, with almost 18% of its GDP contributing to healthcare costs. One of the focus areas of cost optimization is the quality of consolidated patient information available to physicians, as it is critical to improving safety processes and quality. Lack of insight into patient information and process management leads to an increase in cost due to length of stay or complication rates. To manage frontline costs, it is imperative to establish a robust data-driven system. Using systems such as EDW will enable physicians to get real-time answers to clinical quality improvement queries, thus giving them the opportunity to analyze LOS and make necessary changes. Simply put, a hospital can improve its productivity by up to 26% by creating an environment for better decisions, thus creating more opportunities to optimize cost.

Supply Chain and Standardization

While focusing on increasing the volume, revenue, and growth of hospitals, one of the areas that is affected due to lack of attention is the supply chain. Gartner reports that the total healthcare supply chain cost averages to 37.3% of the total cost of patient care. The supply chain in a hospital can be affected by inefficiencies, service duplication, and poor labor management. To optimize costs in this segment, hospitals must focus on standardizing processes, manage physician schedules, and leverage health systems to handle patient access and flow. To build resilience within the healthcare supply chain, the top management must implement preventive measures such as improvements to data analytics and supplier visibility, and external intelligence focused on the healthcare supply chain.

Having discussed the different focus areas for cost optimization, it is important to implement these strategies wisely. While implementing a cost optimization strategy, one must follow these four rules:

  • Have a clearly defined area of focus
  • Build a functioning operating model
  • Learn and implement the right lessons
  • Demonstrate sustainable value

According to Gartner experts, avoiding reactionary cost-cutting in favor of purposeful prioritization requires a partnership between CIOs, CFOs, and CEOs. To that end, Gartner recommends the following:

  • Adopt scenario planning strategy to identify processes and technology impacts using various parameters such as TCO, ROI, and value creation
  • Expand IT portfolio in clinical processes using RPA, ML, AI, and NLP capabilities
  • Evaluate, review, and protect funding of transformative technology projects in different areas such as administrative modernization, patient engagement, etc.

Celebrating Inspirational Women

Rajeswari S

“Each of us has that right, that possibility, to invent ourselves daily. If a person does not invent herself, she will be invented. So, to be bodacious enough to invent ourselves is wise.”  – Maya Angelou

Yet another International Women’s Day is around the corner! Every year, our strength, perseverance, and glory reach newer heights. I would like to take this opportunity to celebrate some inspirational women.

The Prestigious Firsts!

All-women crew

Captain Zoya Aggarwal, Captain Papagari Thanmai, Captain Akansha Sonaware and Captain Shivani Manhas of the all-female pilot crew of Air India made history by completing the longest non-stop commercial flight ever. They covered more than 8,600 miles and flew over the North Pole.

Kamala Harris

She is the United States’ first female vice president, the highest-ranking female elected official in U.S. history, the first African American vice president, and the first Asian American vice president. Kamala Harris became the Vice President upon inauguration in 2021 alongside President Joe Biden in the 2020 US election.

Women with Amazing Minds and Hearts

Shalini Saraswathi

  • A modern-day woman, balancing her corporate job, blogging, and fitness.
  • A blade marathon runner and an adventure enthusiast.
  • Lost both her arms and legs to a rare form of bacterial infection. Hard work, focus, and perseverance became a pole of strength. She soon completed a 10k marathon with an outstanding record of 1 hour and 35 minutes!
  • Awarded several times with the ‘Iconic Woman Award’. 

Vandana Shah

  • At 28, abused by in-laws and thrown out of her marital home at 2 am; had little money, nowhere to go, and no one to turn to.
  • Today, a leading divorce lawyer and the founder of India’s first non-judgemental divorce support group that provides a positive perspective and focuses on rebuilding life even while going through a divorce.
  • Author of 360 Degrees Back to Life – a Litigant’s Humorous Perspective on Divorce.
  • Launched the world’s and India’s first legal app, DivorceKart, which aims to answer all legal queries regarding divorce instantly.

Daniela Rus

  • Romanian-American roboticist, an MIT professor and the first female head of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), one of the largest and most prestigious AI research labs in the world.
  • Rus’ ground-breaking research has contributed immensely to networked collaborative robots (robots that can work together and communicate with one another), self-reconfigurable robots (robots that can autonomously change their structure to adapt to their environment), and soft robots (robots without rigid bodies).

Leila Jana

  • A social entrepreneur and a great young Indian origin humanitarian.
  • Pioneer in the field of impact sourcing.
  • Leila founded Samasource in 2008 with the mission of giving work, not aid, by hiring workers in impoverished areas, training them in AI data annotation, and providing the technology to plug their skills into the global digital economy where they could earn living wages. 

Pappammal

  • 105-year-old Pappamal, a centenarian from Tamil Nadu, India, was conferred the Padma Shri (fourth-highest civilian award in India) for her work in organic farming for the past 70 years!
  • Does organic farming in about 2.5 acres of her land; cultivates a variety of crops including millets, bananas, and okra.
  • A part of the TN Agricultural University’s advisory committee, and keeps abreast of the latest developments in organic farming by taking part in conferences.

Why Women make Great Leaders

While we see men and women leaders run several successful businesses, does the word “leadership” mean the same to them? A survey conducted by a US talent management solutions company says, 65% of women (versus 56% of men) said they view leaders as those who share their knowledge and connect with their colleagues to help the team and business. When women bring this attitude into managerial roles, it makes them more effective as leaders.

Emily He, Oracle’s Sr. VP of the Human Capital Management Cloud Business Group says “In contrast to men, who tend to be career-centric and want to maximize their financial return from work, women view work more holistically, as a component of their overall life plan. They’re more likely to approach their careers in a self-reflective way and value factors such as meaning, purpose, connection with co-workers and work-life integration.”

Hear it from other women leaders too.

On being nurturing

“One of the key aspects of leadership is the ability to help your team members develop their own skills and strengths. Women are naturally nurturing, which in the best scenarios can translate to helping those around you succeed.” – Marilyn Heywood Paige, VP Marketing, FiG Advertising

On valuing work-life balance“We are able to balance professional and personal leadership skills. It’s easier to approach a women leader with a personal request, or a sensitive question. I care about my team and their well-being. I also find women more proactive in becoming mentors, and sometimes it’s already such an open and communicative relationship that the transition to mentor is easy.” – Amy Killoran, Creative Manager, I Love Travel

On wearing many hats

“They often balance careers, households and even aging parents, among other things. Women pivot, adjust and focus on solutions. Resting in the doom and gloom can be time-consuming, so many shift to find positive solutions to life and work problems.” – Gretchen Halpin, Chief Strategy Officer, Hewins Financial Advisors

We’re Tough, We’re Ambitious, We’re Different!

References:

About the Author –

Rajeswari is part of the IP team at GAVS. She has been involved in technical and creative content development for the past 13 years. She is passionate about music and writing and spends her free time watching movies or going for a highway drive.

Health Information Exchanges in Post-Pandemic Healthcare

Srinivasan Sundararajan

Electronic Health Information Exchange (HIE) allows doctors, nurses, pharmacists, other health care providers and patients to appropriately access and securely share a patient’s vital medical information electronically – improving the speed, quality, safety, and cost of patient care.

HIE enables electronical movement of clinical information among different healthcare information systems. The goal is to facilitate access to and retrieval of clinical data to provide safer and more timely, efficient, effective, and equitable patient-centered care.

While the importance of HIE is clearly visible, now the important question is how hospitals can collaborate to form an HIE and how the HIE will consolidate data from disparate patient information sources. This brings us to the important discussion of HIE data models.

HIE Data Models 

There are multiple ways in which an HIE can get its data, each influencing the way in which the interoperability goals are achieved, how easily an HIE platform is built and how to sustain in the long run especially if the number of hospitals in the ecosystem increases. The two models are

  • Centralized
  • De-centralized

Centralized HIE Data Model

ehr modernization services with healthcare data

This is a pictorial representation of centralized HIE data model.

As evident, in the centralized model, all the stakeholders send their data to a centralized location and typically a ETL (Extraction, Transformation, and Loading) process ensures that all the data is synced with the centralized server.

Advantages

  • From the query performance perspective, this model is one of the most efficient, because the DBAs have complete control of the data and with the techniques like partitioning, indexing they could ensure that the query can be done in the best possible manner. Since the hardware is fully owned by a single organization (which is the HIE itself), this can be scaled out or scaled up to meet the demands of the business.
  • This model is fairly self-sufficient once the mechanism for the data transfers are established, as the need to connect to individual hospitals are no longer there.
  • Smaller hospitals in the ecosystem need not take the burden of maintaining their data and interoperability needs and can just send their data to the centralized repository.
  • Better scope for performing population predictive and prescriptive analytics as the data resides in one place and easier to create models based on the historical data.

Limitations

  • This model needs to have highest level of security built in, because any breach in the system will compromise the data of entire ecosystem. Also considering that individual hospitals send their data to this model, all the responsibility lies with a single agency (HIE) which is highly prone to lawsuits related to data privacy and confidentiality.
  • There is no control for patients in managing their own records and right to provide consent for data access, even though this information can be collected there is no easy way to implement them.
  • The system is prone to a single point of failure and hence require efforts for high availability of the platform.
  • This model will face scalability challenges as the network grows beyond a point, unless the platform is modernized with latest big data databases, the system will have scalability issues.
  • Lot of coordination required to monitor the individual ETL jobs for their success, failure, and record synchronization details, so this model will have a huge allocation of IT resources and will increase the total cost of ownership.
  • The model of expense sharing between the HIE, data producers, data consumers will be difficult and needs to have a strong governance model.
  • Difficult to match the patient information across hospitals, unless the both the hospitals use deterministic matching attributes like SSN, otherwise it would be difficult to match between patients who have misspelt names, different addresses, etc.
  • This model may suffer data integrity issues when the participant hospitals merge with each other, such that the IT systems of the two hospitals need to take care the internal details of the ETL jobs.

De-centralized HIE Data Model

ehr modernization with health data management platforms

The above is the pictorial representation of decentralized HIE data model.

As evident, in this model individual hospitals, continue to own all their data, however, the centralized database keeps a pointer – MPI (Master Patient Index), which serves as a unifying factor for consolidating data for that patient. While some books also suggest a variant called Hybrid model which combines centralized and decentralized data models, we believe that pure play decentralized model itself is a hybrid (i.e. centralized + decentralized) because there needs to be a centralized repository to keep the master patient index along with all the access rights and related information.

Advantages

  • It is much easier to implement as no huge investment is required from a centralized provider perspective. The HIEs in this model can start low and grow on demand basis
  • Less expensive as no single organization owns all the data, but only a pointer to the data, and the respective hospitals continue to own the data.
  • Much easier to provide patients the control of their own data and patient’s consent can be a key in accessing information from the respective hospitals.
  • No need to worry about broken ETL jobs and the latency between source and destination. All the data is always current.
  • No need to worry about single point of failure, as the individual sub systems will continue to exist even if one link to a particular hospital is broken. Maintaining the high availability of this light-weight platform is much easier than a monolithic large database as part of a centralized data model.
  • A data breach into the centralized repository still will not compromise all the data, as the individual hospitals are likely to have some more additional controls which prevent a free flow for hackers. This also prevents one organization from facing all the legal issues resulting from patient data breach.

Limitations

  • This model will have a query performance problem when it comes to aggregating a patient information across multiple hospitals because each has to be obtained with a separate API call and a facade has to group multiple datasets.
  • Difficult to establish common standards in terms of data formats and APIs across multiple hospitals, this may result in each hospital having their own methods.
  • Bringing all the stakeholders including the patients to agree on a MPI (Master Patient Index) will have governance challenges and needs to be implemented carefully.
  • Providing analytics for a large set of population will have challenges due to the difficulties in consolidating the data.

GAVS Point of View & Role of Blockchain

While no model can be 100% perfect for building an HIE, GAVS’ analysis point to that fact decentralized model of building and operating HIE is better than centralized model.  The COVID pandemic has changed the world and the boundaries of healthcare no longer exist within a smaller geography or neighbourhood as it used to. More the participants and bigger the network size, the better it is for population health improvement initiatives. Also, in high population countries where there are initiatives like national healthcare for all, these larger initiatives cannot be done using a pure play centralized model.

From an implementation perspective, the Healthcare IT world has been curiously watching the role of Blockchain in data interoperability and in the implementation of decentralized HIE. Blockchain which is a distributed database has decentralization built-in as part of its core architecture. It would be easier to implement decentralized HIE using blockchain.

GAVS Reference Implementation Rhodium to cater to Healthcare Data Management and Interoperability has positioned Blockchain as a core mechanism for patient data sharing, we will share more of our thoughts and details of reference implementation in the coming articles in this series.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi Modal databases, Blockchain and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero knowledge proofs.