Do Patient Decision Support Interventions Lead to Savings?

Publicity surrounding the implementation of patient decision support interventions (DESIs) traditionally focuses on two areas of improvement: helping patients make better decisions AND lowering health care spending. The use of patient decision support interventions as a means to generate health care savings has been widely advocated, but the extent and quality of evidence is unclear.

A systematic review found that the evidence for savings was not as broad or deep as suspected. In addition, an examination of the quality of the economic analyses in the studies was performed. Not surprisingly for a young field, the quality has room for improvement. An assessment of the risk of bias in each study found a moderate to high risk across the studies that found savings.

Led by Thom Walsh, a post-doctoral fellow at the Dartmouth Center for Health Care Delivery Science, the team included Paul James Barr and Rachel Thompson, also postdoctoral fellows at the Dartmouth Center for Health Care Delivery Science, Elissa Ozanne, associate professor at the Dartmouth Institute for Health Policy & Clinical Practice, Ciaran O'Neill, professor at the School of Business and Economics, National University of Ireland, and Glyn Elwyn, professor at both the Dartmouth Center for Health Care Delivery Science and the Dartmouth Institute for Health Policy & Clinical Practice.

Their objective was to perform a detailed systematic review of a wide range of studies to assess DESIs' potential to generate savings, given a concern that premature or unrealistic expectations could jeopardize wider implementation and lead to the loss of the already proven benefits. The ethical imperative to inform patients is, in the authors' views, paramount. Although there is good evidence to show that patients tend to choose more conservative approaches when they become better informed, there is insufficient evidence, as yet, to be confident that the implementation of patient decision support interventions leads to system-wide savings.

After reviewing 1,508 citations, seven studies with eight analyses were included in the analysis. Of these seven studies, four analyses predict system-wide savings, with two analyses from the same study. The predicted savings range from US $8 to $3,068 per patient. Larger savings accompanied reductions in treatment utilization rates. The impact on utilization rates, overall though, was mixed. Authors used heterogeneous methods to allocate costs and calculate savings.

Dr. Walsh said, "Our review tells us the ability for decision support to lead to savings is still undetermined. There are many other reasons for the use of decision support. We are concerned the benefits could be lost if promises of savings are unfulfilled."

This article is published in the BMJ (British Medical Journal). BMJ 2014;348:g188

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...