Strategic Data Management for Successful Healthcare Outcomes

Hema Lakkaraju

• Publisher: Business Expert Press
• Year: 2021
• ISBN:
  9781637421505
• Topic: Negocios y empresa
• Subtopic: Industria farmacéutica, biotecnológica y sanitaria

Strategy is paramount for successful modern healthcare data management.

The healthcare landscape continues to evolve in an effort to accommodate our ever-connected world. A digital healthcare system poses new challenges and exposes existing issues as professionals—like you—strive to solve concerns. This book recognizes the unique tasks of dedicated professionals while attempting to decrease confusion on this key topic.

It's time to discuss why strategy is important for modern healthcare data management, how strategy can create new business or upscale a business in healthcare data management, and how these tactics assist your business in gaining a competitive advantage.

Cut through the frustration generated by the staggering amount of healthcare data currently being created, collected, and distributed—this book will teach you how.

This book will help you to understand:

• Critical types of data
• How to strategically manage data
• How to build better patient care
• Tips for improving performance
• New ways for your business to thrive

And so much more…

CHAPTER 1

Modern Healthcare

Health data has taken multiple forms over years. Its importance and impact are undeniable over thousands of years.

History of Healthcare Data Management (HDM) Transformation

Data record keeping for healthcare is not a modern concept. Health records have been found on clay tablets inscribed with the ancient language of cuneiform conveying patient information to keep a record of the patient condition and treatment given. These records date back about 5,000 years to the Mesopotamians. The Egyptians continued this data record-keeping process for thousands of years transcribed on papaya for patient care tracking and condition evaluation for future medical reference. These records were safe from misuse as only learned professionals could read and write so the security and privacy of a patient at these times was not a concern.

Fast forward 2,000 years, and the process of healthcare record keeping was the same as the Egyptians: Now records were kept on paper in file folders in file cabinets in siloed doctors’ offices and hand carried between different providers keeping the records safe but having limited benefit to the healthcare community. The jump from clay tablets, papaya, and paper to globally networked digital database servers housing electronic health records (EHRs) did not come until very recently in this healthcare record-keeping timeline. In the 1960s the idea of adding additional information to help track the life cycle of the patient so that others could pick up the care and treatment with a full understanding of the patient’s history and response to care came into play. Up until this time, healthcare records usually recorded only the diagnosis and the treatment provided. The concept and reason behind healthcare record keeping had not changed over the years but with the newly formed concept of the “problem-oriented” record keeping, healthcare records would change adding supplemental information that would form the foundation of modern data sets, and those data sets would begin to play a big role in the coming record-keeping revolution. This would also introduce some concerns around the security of the patient information, as illiteracy was no longer a safeguard.

Once this concept of problem-oriented record keeping was introduced the idea was that preventative action could be taken as you now had cause, treatment, and effect forming the first data points in healthcare record keeping and those data points could be compared to other patients for improved outcomes. The new data points were proving helpful in preventative medicine but the access to the records and the cross-referencing of patient records for researchers was simply too difficult for practical outcome gains. The use of digitally stored data made it easier to look at and extract useful data and the advantage of storing records in an electronic format made the storage and retrieval of the records easier but the full conversion to digital records for every patient away from paper would take a few more years.

The recognition behind the value of electronic records was driven by the need for greater efficiency of access to the data collection, the volume of the potential data, and the potential for positive patient outcomes from that data analyses. The development of an EHR database improved the patient health record data collection, storage, access, and cross-referencing, which were made easier by an order of 100× magnitude. Now healthcare had the ability to cross-reference all aspects of a patient’s medical encounters with great specificity and compare conditions, cause, treatment, and outcomes to improve outcomes for the broader population. The limiting factor in this revolution of healthcare record keeping was that the information on patients was kept on server databases isolated to the institution providing care for those patients.

The next step was to expand the access to the records for a broader cross-section of patients from around the world.

Well, the computational world is moving quickly toward the use of quantum bit processors for processing the vast amounts of data that have been compiled in healthcare databases around the world. And because of the development of quantum computing (QC), the quantum key distribution (QKD) encryption model is necessary. Securing the data of the communication becomes more difficult. The very thing that makes the QC so valuable to healthcare makes it a threat to healthcare. The quantum computing (QC) can not only improve outcome for medical diagnostics, but also be used to break the longest encryption strange available by a binary processor in a matter of seconds rendering the encryption of the data useless. The value quantum bit processing brings to healthcare initially takes the form of an improved provider medical resource for diagnosing and treating patients. Data processing request from a practitioner concerning the diagnosing of a condition and determining the best course of action for treatment takes time, and in some cases, the patient does not have the luxury of time. A quantum computer can process all the data available in the world and return an assessment to the provider in a matter of seconds as opposed to several minutes or even hours depending on the data set query. Researchers can use extensive and seemingly disjointed data queries and produce previously unimagined data results advancing preventative healthcare initiatives on orders of magnitude in minutes or hours instead of weeks and months.

Roger Santos’ interview shares above glimpse of the past, present, and future of healthcare.

At present, in this fifth industrial revolution, innovation is occurring with an inclusion of people and machines. Fifth industrial revolution brought in the need and want for personalized healthcare by people. This need can be performed successfully through strategic data management.

It is very clear that technology and tools changed more frequently yet data at the core and its value are irreplaceable. It is very important to create a health data management which can provide high value and performance beyond technology changes.

The Present Healthcare Data and Their Challenges

There are healthcare challenges at present which include adaptability, agility, interoperability, and implementation of these digital technologies in the healthcare system.

Mobile Network Coverage

Mobile health technology can be adopted by people only when they have the basic mobile network coverage. Yet, unfortunately, every one in five Americans living in rural areas lacks mobile phones through which they can go online and access digital facilities. While the number of mobile devices in rural American areas has sharply increased, adults in these areas are still 10 to 12 percent less likely to have a smartphone than in urban or suburban areas.

There are noticeable gaps between the network usage of rural versus urban American populations alone, making one wonder about the status of other nations.

Mobile network coverage is a big feature in adopting mobile health technology. Without it, a digital healthcare system is hard to imagine. Many major organizations, nowadays, are allying to improve the situation. The Federal Communications Commission is on phase II of its Connect America Fund, a plan to expand broadband access and mobile coverage for rural communities in the United States [1]. These efforts will need to be consistent if progress is to be expected on any digital fronts. It might take some time for the rural area to be fully functional and take full advantage of the mobile health products.

Expanding the footprint of the mobile network might bring more adaptability for mobile health as it has become the core necessity during the current pandemic and for the future as well.

The need for such technology and network was made apparent with the emergence of the COVID-19 worldwide pandemic. A greater collaboration was needed between countries to research the virus and get a vaccine created in time. The virus strand was new, and information was needed to better understand it: how it affects people, the levels of severity in the affected, its cure, and what can be done to avoid it. Digitalization was instrumental in our fight against the pandemic, providing the tools to share massive volumes of medical data across distances and process valuable insights out of them. It also opened the possibility of remote medical assistance, reducing some of the risks for frontline medical workers.

Yet, it is worth noting that digital health innovation was already making unmatched progress even before the pandemic. With a range of products, mobile apps, telehealth, telemedicine, and more, the pace at which innovations are being made projected a sum close to $207 billion in investment by the year 2026 [2].

Internet Access

While the dwellers of cities and metropolitans have taken the speed and availability of our Internet connections for granted, it is not the same for everyone around the world. In urban centers, the provision of Internet connections and broadband is present and being used for a plethora of purposes such as telecommunication, data storage, data sharing, and a constant exchange of information, but things are different in the rural and underdeveloped areas.

While our digital health products function seamlessly, rural areas are far behind in this walk toward a more sophisticated future. According to data from the Federal Communications Commission, 39 percent of people living in rural areas in the United States lack access to high-speed broadband [3]. It is a significant percentage: Almost half of the rural population is without the necessity for making up the foundation of a digital healthcare system. Considering the lack of what is an essential resource in a population this big, it becomes difficult to imagine a complete transition to digital. Hospitals and clinics cannot, as a result, maximize the usage of the digital health products devised with such zeal and fervor in these rural areas.

Low Internet penetration remains a key issue of healthcare in this digital era. If progress must be made on a broader scale and with the inclusion of all populations, urban and rural, then the issue of Internet access would have to be solved beforehand and with efficiency.

Data Centricity

While the world is producing data on all fronts, in ever-increasing quantities, and is coming up with more digital health products, services, and management tools than ever before, we need ways and resources to share this data on a central server to benefit one another and enhance efficiency. In the healthcare sector, data comprises lab results, genomics data, vital signs, medications, medical images, and more. It has accumulated into a tremendous amount in total. Around 25 exabytes (1 exabyte = 1 billion gigabytes) worth of medical data is present to date.

Dan Demers, entrepreneur, and successful IT executive, explains the present data management challenges and how data centricity can bring value. Health data sets are from different sources and are often siloed. These siloed datasets from multiple systems raise the concerns around sharing and collaboration. The traditional approach of sharing includes data copies or duplicates into other systems. This can bring in the control and security issues.

Data centricity can:

• Reduce the operation cost and time for multiple systems

• Provide clear visibility of data from multiple sources

Collaboration is the key and need. In healthcare, collaboration is under scrutiny due to its privacy and security concerns. Modern governance includes data centricity which can enhance safe and secure data-centric collaboration efforts at a global scale.

Centralized data can open the doors to understanding health data better, create a smart health ecosystem, and create patient-centric healthcare as information would be dispersed and deployed.

To create a safe, secure, smart, and sustainable healthcare ecosystem, healthcare needs a clear sense of the idea of health data with respect to the patient. This becomes a big challenge if data is sprawled and scattered. Creating a centralized patient-centric health data system thus becomes the foundation for delivering excellent healthcare. This scattered data can be gathered via centralization. In addition, more tools and data-centralizing platforms need to be made for better data management.

SaaS platforms, tools, and apps that can help us with medical data processing are already present, but the issue is that they do not gather all the information present in one place. This potentially results in data sprawling. Data sprawling is another term for scattered data in multiple systems. Data sprawling creates new challenges, especially in visible patient data. As healthcare and patients are starting to generate more and more data from digital health products and services, there is a greater need to bring it all into one place. The challenges around data visibility and management also need to be met head-on.

Legacy and Siloed Data Systems

In an interview, Pawan Kohli explains that in most of the cases about the systems and infrastructures, many local and mid-sized hospitals are still running on older systems which might be missing several things or might just not have the budget as well as the resources to upgrade. The systems are old, there are various system compatibility issues, and the understanding of health IT systems is weak with little patient privacy. It is not that there is trouble with patient privacy, but a lack of the infrastructure involved could lead to a leak in patient privacy.

Another problem that comes up is the fact that the systems are not just extremely old but have tech systems in miserable conditions, and in some cases, completely nonexistent. The systems are also not integrated and rather detached from one another, unlike the desired interoperability. These systems need to be replaced, and that must be the final step into this roadmap as a new, digitized healthcare system evolves and doesn’t just evolve, but becomes an essential part of how healthcare operates, is viewed, and wonders in many ways.

When physicians Dr. Pitta, Dr. Thota, and Dr. Konidena were interviewed, it was clear that there are still challenges that are unanswered:

• For small- and mid-sized hospitals, interoperability is still a challenge. The efficiency and poor interface demand more time for clinical-type data entry and takes valuable time from patient care. This can lead to poor satisfaction for patients and physici...