{"id":28544,"date":"2022-02-23T06:00:00","date_gmt":"2022-02-23T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28544"},"modified":"2022-02-24T09:41:39","modified_gmt":"2022-02-24T17:41:39","slug":"insidebigdata-guide-to-how-data-analytics-is-transforming-healthcare-part-3","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/02\/23\/insidebigdata-guide-to-how-data-analytics-is-transforming-healthcare-part-3\/","title":{"rendered":"insideBIGDATA Guide to How Data Analytics is Transforming Healthcare \u2013 Part 3"},"content":{"rendered":"\n

Sponsored Post<\/em><\/p>\n\n\n\n

Data-driven healthcare<\/strong><\/p>\n\n\n\n

When the average person imagines cutting-edge medical research, they picture doctors and scientists in white coats surrounded by chemicals, beakers, and specialized equipment. But the truth is that some of today\u2019s greatest medical innovations come from a very different setting\u2014people sitting at computers running analytics software.<\/p>\n\n\n\n

This is no secret to people involved in the healthcare industry, of course. Administrators, physicians, researchers, and students are all aware that data analytics is in the process of transforming healthcare around the world. In fact, a Stanford Medicine report on \u201cThe Rise of the Data-Driven Physician<\/a>\u201d revealed that \u201cnearly three-quarters of all medical students and nearly half of all physicians are planning to pursue additional education in data-oriented such as advanced statistics and data science.\u201d<\/p>\n\n\n\n

In addition, organizations are investing vast sums in analytics technology. Market researchers say that the global healthcare analytics market<\/a> will likely be worth $39.7 billion in 2022 and is increasing at a 19% compound annual growth rate (CAGR).<\/p>\n\n\n\n

Similarly, a Huron Consulting Group report titled \u201cEmbracing Healthcare\u2019s Digital Transformation<\/a>\u201d found that nearly half of healthcare leaders surveyed (47%) are looking to data management and analytical decision making to help their organizations grow.<\/p>\n\n\n\n

However, that same report added, \u201cDigital, technology, and analytics strategies exist for nearly all organizations, yet only 30% have begun to execute on those plans.\u201d<\/p>\n\n\n\n

If your organization is one of the many that is still in the early stages of implementing its big data analytics strategy, you likely still have many unanswered questions. This technology guide, \u201cinsideBIGDATA Guide to How Data Analytics is Transforming Healthcare<\/a>,\u201d sponsored by Dell Technologies<\/a>, provides an overview of some of the trends influencing big data in healthcare, the potential benefits, likely challenges, and recommended next steps.<\/p>\n\n\n\n

Challenges of big data in healthcare<\/strong><\/p>\n\n\n\n

In order to realize all the potential benefits of big data analytics, healthcare organizations will also need to overcome some significant obstacles. Here are five of the most significant:<\/p>\n\n\n\n

REGULATION<\/em><\/p>\n\n\n\n

Because of the need for patient privacy, healthcare is one of the most heavily regulated industries on the planet. Any project that deals with patient or institutional data will require strict governance to make sure that it complies with all relevant regulations.<\/p>\n\n\n\n

In some regions, such as the European Union, governments prevent private data from being stored or processed outside the home country. This can make it difficult or inadvisable to use public cloud computing services for data storage or analytics. Organizations may also need to anonymize and\/or encrypt data in order to comply with relevant regulations. And in many cases, they will also need to have policies and procedures in place that allow patients to access and potentially delete their personal data should they choose to do so.<\/p>\n\n\n\n

LACK OF STANDARDIZATION<\/em><\/p>\n\n\n\n

Different organizations record and store data in different formats. That can make it extremely difficult to combine data from different sources and come up with any meaningful conclusions.<\/p>\n\n\n\n

For example, a Wall Street Journal article<\/a> about efforts to create a cancer database reported that the agency involved \u201cfound more than 60 different versions of how white blood cell counts\u2014a fundamental biomarker for cancer patients\u2014were recorded.\u201d The amount of data cleansing required to standardize data in these situations can be daunting. However, some regulation that promotes interoperability among EHRs is helpful to improve the situation\u2014albeit slowly.<\/p>\n\n\n\n

DATA QUALITY<\/em><\/p>\n\n\n\n

The conclusions you can draw from your big data analytics are only as good as the data feeding those solutions. Poor data quality and biased data are problems that plague analysts in all industries, but it can be particularly problematic in healthcare, where the stakes are so high.<\/p>\n\n\n\n

Harvard\u2019s Xiao-Li Meng<\/a>, the Whipple V.N. Jones Professor of Statistics, says that doing an analysis based on biased data is worse than doing no analysis at all. \u201cIf you have the resources, invest in data quality far more than you invest in data quantity,\u201d Meng said. \u201cBad-quality data is essentially wiping out the power you think you have. That\u2019s always been a problem, but it\u2019s magnified now because we have big data.\u201d<\/p>\n\n\n\n

DATA SILOES<\/em><\/p>\n\n\n\n

Another chronic problem for healthcare organizations is the existence of data siloes. Organizations use a lot of different systems and applications that all have their own data stores. Most healthcare big data projects require significant effort in getting information from the places where it resides into a centralized system where it can be combined with other data and analyzed.<\/p>\n\n\n\n

HARDWARE REQUIREMENTS<\/em><\/p>\n\n\n\n

In order to process the workloads generated by big data analytics, you need the right infrastructure. That means flexible servers with fast processors and GPUs that can handle the demands of machine learning and predictive analytics. You\u2019ll also need fast storage and hardware designed to scale out as your data volumes continue to grow.<\/p>\n\n\n\n

What should you do today?<\/strong><\/p>\n\n\n\n

If your healthcare organization is like most, you have probably begun to dabble with big data analytics, but you have far to go before you are maximizing the potential benefits. The good news is that it is possible\u2014and even preferable\u2014to start small and then scale your efforts.<\/p>\n\n\n\n

Experts recommend the following steps:<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

STEP 1 \u2013 ENGAGE YOUR STAFF<\/em><\/p>\n\n\n\n

Big data analytics has the potential to benefit nearly every department in your organization. Start by discussing the possibilities with your staff members and get their ideas for how big data analytics might help your organization accomplish its goals and become more efficient. Your workers are your best source of insights into the questions you need your analytics to answer, and engaging them early can help you generate support that will be critical in later stages of your efforts.<\/p>\n\n\n\n

STEP 2 \u2013 LOOK FOR \u201cQUICK WINS\u201d<\/em><\/p>\n\n\n\n

One of the biggest mistakes that healthcare organizations make in regard to big data analytics is that they try to do too much too soon. Instead of trying to wrangle all your organization\u2019s data into one massive data lake, begin by identifying opportunities for significant improvements that require a smaller amount of effort. These pilot projects are much more likely to generate a positive return on investment, and you can complete them much more quickly. They also help build your team\u2019s expertise and the excitement around doing more with your data. Once you have a few successes under your belt, you can think about tackling a larger initiative.<\/p>\n\n\n\n

STEP 3 \u2013 GET THE RIGHT INFRASTRUCTURE IN PLACE<\/em><\/p>\n\n\n\n

To support your big data analytics initiative, you\u2019ll need to have the right combination of hardware, software, and\/or services in place. Your IT team will need to conduct a thorough review of the assets your organization already has, as well as an assessment of what will be necessary to support your efforts as they grow. Performance, flexibility, and scalability will be key. You\u2019ll also need to keep your compliance requirements in mind, which may limit your ability to use cloud services. (See sidebar.)<\/p>\n\n\n\n

STEP 4 \u2013 SCALE OVER TIME<\/em><\/p>\n\n\n\n

As you experience success, you\u2019ll want to make big data analytics tools available to more teams within your organization. Use what you learned in your initial projects to optimize your later efforts. As your efforts expand, you\u2019ll find more useful data and more ways to apply your analytics tools to answer questions that are important to your organization.<\/p>\n\n\n\n

STEP 5 \u2013 CONTINUE LEARNING<\/em><\/p>\n\n\n\n

A big data analytics project is never truly finished because the data volumes continue to grow and the available technology continues to improve. New advances in artificial intelligence and machine learning are making new approaches possible on an almost daily basis. By staying abreast of the latest developments, your team will be better positioned to take advantage of new opportunities as they emerge.<\/p>\n\n\n\n

The Huron Consulting Group report summarized the current situation well when it said, \u201cThe acceleration of digital tools, technology, and analytics within healthcare continue to be the industry\u2019s greatest challenge and opportunity. Looking ahead, digital tools and data analytics will underpin the transformation of all aspects of business operations and care delivery.\u201d You can help keep your organization at the forefront of this transformation by investing in big data analytics technology and supporting your staff as they implement analytics projects.<\/p>\n\n\n\n

For more information, visit delltechnologies.com\/healthcare<\/a>.<\/p>\n\n\n\n

Over the past few weeks we\u2019ve explored these topics:<\/p>\n\n\n\n