Can Technology Boost Efficiency in Healthcare? Plus, A Look at Companies Leading the Way

by Jolly Nanda | Aug 13, 2020 | Health Plans, Technology

Can Technology Boost Efficiency in Healthcare? Plus, a Look at the Companies Leading the Way

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

It is currently estimated that anywhere between 20% and 50% of the U.S. healthcare system costs are due to inefficiency. The troubling part of this statistic is that money could be going in several other places such as investments into healthcare startups or preventative care plans. These excess costs directly affect consumers who, in the field of healthcare, are also patients. However, we now live in a time of increased technological capabilities and, paired with the power of analytics, it can help us decrease redundant care, improve transitions of care and advance provider-provider and provider-patient communication in order to decrease cost waste in the healthcare industry.

Discussion

One of the biggest cost concerns in the healthcare field is communication – paper, phone calls and faxes are all big contributors to inaccuracies and miscommunications. Albert Santalo, the founder of CareCloud, believes technology can bridge a huge gap here. CareCloud is a cloud-based electronic health record provider that hopes to cut the costs of inefficiency by creating a platform that allows for cross communication between providers, billing and consumers. Another big concern in cutting costs in healthcare is the timely entry of data. Hill-Rom Holdings is another company making great strides in the field of healthcare with their smart hospital beds. Their beds ensure that vital signs are entered and time stamped immediately, rather than up to hours later when providers get a chance to enter data into their system. This technology saves money in healthcare by providing physicians with the ability to make accurate and timely decisions regarding a patient’s care plan. Eventually, analytics can further support the goal of accurate decisions further by applying machine learning techniques to the smart bed.

Another use for analytics in healthcare is to improve the timeliness of when a patient is transferred from the ICU. The current system for transferring ICU patients is reactionary and subject to error but applying analytics to this process can not only reduce costs but can also prevent deaths. This is because opening up ICU beds can make space for those who need it more and because some patients may receive better, more specialized care in another unit.

Conclusion

When it can be estimated that up to half of an industry’s costs are due to waste and inefficiency, it is clear that something should change; when that industry is the healthcare industry, it is clear that something needs to change. There should be no room in healthcare for waste or inefficiency because this is an industry that deals with people’s livelihoods and well-being. Thankfully, the rise of analytics and technology helps create cost-solutions that prevent waste and inefficiency and can improve the lives of patients across the nation.

Prescriptive Analytics

by Jolly Nanda | Aug 5, 2020 | Analytics, Covid, Industry Trends

Big data healthcare concept vector illustration.

Prescriptive Analytics

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Here at Altheia Predictive Health, we are constantly improving techniques for disease prediction and diagnosis to improve the lives of people around the world. Where we leave off, companies like IBM pick up and begin applying analytics to the next step in the disease management process which is a treatment plan. Prescriptive analytics in healthcare can refer to several different things but in this article, we will look at prescriptive analytics as it related to the study of prescribed steps for those with chronic conditions to better understand how to manage disease conditions at various stages of a given condition. This can be an extremely useful tool for doctors who are looking at a borderline case and deciding whether or not to begin medication treatments by giving them the ability to see compiled and analyzed data for similar cases.

Discussion

After being diagnosed with a disease, the next question patients and providers have is what steps can be taken to prevent progression of the disease and manage the negative effects of a disease; for some people this is achieved through diet and exercise, for others it may be a prescribed medication and it is highly dependent on several patient factors. Prescriptive analytics is rooted in how to best solve a problem – if the problem is diabetes, a prescriptive model would look at variables in a patient’s file to determine if the problem can be mitigated through diet and exercise. If not, the model would then provide a game plan for how to begin prescribing medicines in the most effective way for any given patient. In diabetes patients, insulin therapy is a common form of treatment where adjustments to a specific individual can be tricky but prescriptive analytics can use the data of similar patients to provide a tried and true treatment plan. Prescriptive analytics can also be used in radiation therapy by weighing various favors, such as treatment location and surrounding organs, to provide a highly targeted treatment plan that treats an area with a calibrated dosage while leaving the rest of the body as unharmed as possible. This is something that we as humans can make educated guesses on but the use of prescriptive analytics is likely much more accurate which means better care for patients.

This field of study also has an incredibly relevant application amidst the Covid-19 Pandemic. Throughout the pandemic data has been collected on patients such as their age, BMI, gender, ethnicity, presence of chronic conditions and more. All of this information combined with their response to treatment plans provides a unique opportunity to apply analytics – by looking at these sets of data together, we can use artificial intelligence to understand which patients respond better to certain treatment plans. This provides a great opportunity to improve Covid-19 survival rates and lower the number of patients suffering from long-term complications as a result of Covid-19.

Conclusion

Prescriptive analytics in healthcare is essentially a way for analytics to troubleshoot disease conditions by providing a data-based treatment plan. By providing physicians with analytical tools, such as prescriptive analytics, we can significantly improve their success rate in treatment plans and as a result improve the quality of life for hundreds of thousands of people.

Blockchain

by Jolly Nanda | Jul 30, 2020 | Uncategorized

Blockchain Technology in Data Management

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Big data in healthcare is a hot topic right now – we constantly hear about how analytics can predict health conditions, the health of a population, the spread of disease, support the efforts of wearable devices and more. There are a lot of cutting-edge developments in this field, however, they will all be undermined if data management in healthcare does not also catch up with these new tools and technologies. In this article we will explore the role blockchain technologies play in healthcare.

Blockchain

One use of blockchain and crypto-technology in healthcare is to increase security and safety of data. Patient records are highly sensitive information that have been exposed to data breaches in the past and that is unacceptable but was hard to prevent in the era before blockchain technologies. Now, highly randomized and privatized algorithms can be used to increase the security surrounding these files and ensure a patient’s safety and right to privacy. This effort has been highly prioritized by nearly every healthcare organization including the United States government. A company working in this area, called Factom, has received $200,000 in grants from the Department of Homeland Security to further research and development in this area (Daley).

Another area in which blockchain can make great strides in healthcare is in communication networks. Currently, miscommunication costs the healthcare industry about $11 billion USD per year mainly due to the current process of coordinating medical records – that is a significant amount of money that could be much better spent elsewhere (Daley). Utilizing blockchain technology to create a means of transferring information privately and accurately should absolutely be a priority when using blockchain technology.

Blockchain can also help protect information in the healthcare supply chain by providing secure and reliable processes to guarantee the authenticity of medicine. This can be done by creating a distribution system that marks a point of origin and keeps track of a shipments location and who it came in contact with. This ensures the medicines delivered to patients are from a legitimate supplier and have not been tampered with. In relation to recent developments in the field of medicine, this can also be useful in tracking the safety of medications and patients if their medication happened to come in contact with an employee who contracted Covid-19 – the tracking ledger can ensure that anyone who came into contact with contaminated products after the infected employee is notified and recommended to be tested.

Conclusion

Blockchain technology is popping up in nearly every industry as a means to accurately and privately transfer information. This type of technology is extremely useful in the healthcare space and has many applications in disease control and prevention. However, another area in healthcare it can impact significantly is in data collection and management. By securing the data of patient’s, healthcare organizations not only ensure the peace of mind of patients but also better diagnoses by ensuring the accuracy of the data communicated. Blockchain technologies can also prevent delays in diagnosis caused by slow-moving communication which continues to support the notion that blockchain in data management is a powerful tool that can greatly benefit patients.

Works Cited

Daley, Sam. “15 Examples of How Blockchain Is Reviving Healthcare.” Built In, 2020, builtin.com/blockchain/blockchain-healthcare-applications-companies.

Discussing Recent Changes in the Availability to Covid-19 Data

by Jolly Nanda | Jul 23, 2020 | Uncategorized

Discussing Recent Changes in the Availability to Covid-19 Data

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Just as we were beginning our weekends last week, our news channels were dominated by headlines detailing the story of how the Trump Administration has stripped the CDC of its access to Covid-19 data. Throughout the Covid-19 Pandemic the CDC has been an integral part of getting key information out to the public through press conferences, visual education material, data reports and more. For those reasons, this development is incredibly surprising and begs the question of who the public should look to for trusted information regarding the spread of and how to protect themselves from Covid-19. This also brings up several issues for hospitals and resource allocation, as well as many other issues for organizations relying on accurate data to make decisions.

Discussion

The first question that you might ask in this regard is – how does the CDC get its Covid-19 data? The CDC was getting its data by hospitals directly sending them information. This move by the Trump Administration tells hospitals to bypass the CDC in data reporting and report information regarding all Covid-19 cases, as well as that hospitals available number of beds and ventilators, to the Department of Health and Human Services. This is incredibly problematic because many of the available models and projections made for Covid-19 have come from private and university researchers who rely on the fact that the CDC makes its data public, but the Department of Health and Human Services does not operate the same way. In fact, this has already impacted many researchers who claim that their models and prediction algorithms began underperforming when access to data was quietly taken away. This begs the question of whether or not who this data will be made available to and if this is simply a method to politicize the information the public is being given regarding Covid-19. Many scientists, physicians and leaders in the field have “long expressed concerns that the Trump administration is politicizing science and undermining its health experts [and feel that] the data collection shift reinforced those fears.”

Michael Caputo, a spokesperson for the Department of Health and Human Services, has said that his department and the CDC would share data so that it remained available to the public, however, many states, including Kansas and Missouri, have not received timely access to relevant data, with some hospitals saying they’ve experienced up to a week of lag time in receiving requested data. As hospitals across the nations are seeing new peaks in their Covid-19 cases, hospital workers have also said that this change in reporting of data is highly disruptive to their usual processes and taking time away from more pressing matters. Some workers also express a sense of frustration knowing that this increase in workload will only lead to less access to data in the future. Additionally, the Trump Administration has repeatedly made claims that the way testing is reported in the United States is leading to confusing reports and information regarding Covid-19. This brings up serious concerns for many physicians who worry that this thought process will lead to false or inaccurate reporting of Covid-19 cases in an attempt to support the goals of the current Administration.

Conclusion

The recent changes in the reporting of Covid-19 cases and relevant information has been widely criticized by leading officials, physicians and the general public as an unnecessary stunt by the Trump Administration. Most have concurred that the decision puts many people at risk by complicating a process that has been in place throughout the pandemic and has supported the public access to and research of Covid-19 data. As we move forward in the battle against Covid-19, we must analyze how we can better access reliable data and provide hospitals with the tools necessary to prepare and succeed in their endeavors.

Works Cited

[1] Stolberg, Sheryl Gay. “Trump Administration Strips C.D.C. of Control of Coronavirus Data.” The New York Times, The New York Times, 14 July 2020, www.nytimes.com/2020/07/14/us/politics/trump-cdc-coronavirus.html.

Florida Covid-19 Predictions

by Jolly Nanda | Jul 16, 2020 | Analytics, Covid, News

Revisiting Our Covid-19 Predictions in Florida: What We Got Right, What We

Missed and What’s Next

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

In our last article we reflected on our predictions regarding the spread of Covid-19. We made observations and predictions in March using linear regression and found that our data had an error rate of less than 10% when looking at the American population as a whole. However, many states had different rules and regulations for social distancing, mask wearing and other precautions, so we also made predictions based on different levels of regulations to predict the impact Covid-19 on hospitals and ICU beds. In this article, we will explore the accuracy of those predictions for the state of Florida and tools that can help us going forward as many states begin to see their number of Covid-19 cases spiking again.

Analysis and Interpretation

In our analysis we made the following predictions for the state of Florida based on different levels of precautions taken:

Predicted Hospitalizations with All Precautions Taken

April	May	June	Mid-June	July	August
2,208	2,028	18, 251	19, 264	10, 139	2,208

Predicted Hospitalizations with Some Precautions Taken

April	May	June	July	August
2,208	10,139	35,487	10,139	2,028

Predicted Hospitalizations with No Precautions Taken

April	May	Mid-May	June	Mid-June	July	August
2,208	20, 278	55, 766	40, 557	10, 139	3,042	2,028

While looking at this data, keep in mind that the number of hospital beds available in the state of Florida is 55,727. Looking back on our data, it’s clear that our model was very conservative. On one hand, we did accurately predict peaks and the fact that Florida would be overwhelmed with Covid-19 cases, however, we predicted that even with no precautions we would begin to see this come to an end in August. As we come to mid-July, Florida is the latest state to break American records with 15,000 new Covid-19 cases in one day which indicates that the end of the battle against Covid-19 in Florida is nowhere near over (Linton). In fact, as of July 7th, 2020, “at least 56 intensive care units in Florida hospitals reached capacity” with another 35 showing less than 10% availability (Chavez).

The natural question to ask when making models such as these is how to improve them. In some ways, our model was conservative – in a no precautions setting, it was almost impossible that Florida could be in back in the same place it was in April. However, some error can be attributed to the lack of knowledge regarding the spread of coronavirus in March. We created these predictions based on Imperial College London’s pandemic model, however, the pandemic model could not account for the various changes, such as when and how certain things would reopen and was also based on the fact that we had a predicted end date. It was also limited in the fact that it was an agent-based model and not a stochastic model, which accounts for randomness such as the random meeting of two people and the impact they would have on their community if they were infected with Covid-19. The most accurate prediction models take in a lot more information about a more concentrated population. For example, it would not try to make every studied population follow the same multipliers because it would consider much more specified information about that population such as the occupations of a population, their ages and activities, their political engagement and beliefs, their methods of transportation, their attendance of religious functions and even more.

Conclusion

What we can take away from this interpretation of data is that there is not a one size fits all model for pandemic predictions. Different counties, cities, states and countries all follow different schedules and habits and while most hospitals in Florida are overwhelmed, there are also likely some that barely see any or limited Covid-19 related issues. For this reason, it is extremely important to consider a lot more information about a more specified population than to use broad, blanketing equations; especially when the data is used for resource allocation. As we continue to battle Covid-19, it is important to take precautions such as wearing a mask in public places, practicing social distancing and maintaining good hygiene.

Prevention

Take a look at the image below to see low to high-risk situations and understand how you can limit your exposure to Covid-19.

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