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The Truven Health Blog


The latest healthcare topics from a trusted, proven, and unbiased source.


Three steps this hospital used to get the right healthcare supply item, in the right place, at the right time — at the lowest possible cost


By Truven Staff/Friday, October 20, 2017

Caldwell Memorial Hospital’s supply chain was struggling, as many hospital operations do, with multiple stock locations, excess and often incorrect inventory, and low accountability for what was on the shelves.

 

So the hospital’s leaders took action, and their successful initiative provides several steps that other providers may want to consider, too.


 #1   Use Lean thinking

Caldwell leaders looked to their prior experience with Lean management tools to guide their efforts in the supply chain. A value stream assessment helped them pinpoint specific challenges, while data collection and analysis helped them develop a strategic plan for tackling them. This critical prep work revealed several key areas of focus: inventory visibility, demand flow optimization and management of physician preference items.

 

#2   Get visual

First up: inventory visibility and demand flow optimization. By introducing a new, Lean-based visual replenishment system, Caldwell gained the transparency needed to consolidate supplies, eliminate excess inventory and lower distribution costs. Plus, clinicians no longer had to spend valuable time managing supplies when they should be with patients. The combined annual savings from these initial activities totaled more than $3 million.

 

#3   Reign in requests

Next on the list: physician preference items. From supplies to lab resources to room and board, no two Caldwell physicians seemed to utilize assets in quite the same way. And these variations were adding up.

 

Digging into and analyzing resource usage data allowed Caldwell to break down the costs by clinician, case and location. This revealed just how much the inconsistency was costing the hospital — more than $4 million — and what Caldwell needed to do to convert those costs into cost-saving opportunities.


Results:


If you’d like more information on how this hospital achieved its remarkable result, please reach out to us. You can also read the full case study here.



Three examples of how performance improvement opportunities were hiding in this health system’s data


By Truven Staff/Monday, October 16, 2017

Like most health systems, Indiana University Health System (IU Health) leaders knew they had large amounts of valuable data stored throughout the organization. The challenge for the largest health system in Indiana was how to use the often-segmented data to quickly identify opportunities for cost controls and financial decision making, especially at the regional level.

To meet this challenge, the system pulled together a results-driven group called the Profitability & Utilization Support Hub (PUSH), made up of experts in revenue cycle, supply chain, clinical operations, labor analytics and more. The team’s charter: Make data-driven recommendations that could eventually drive operational and financial improvements.

The PUSH group began to identify opportunities by leveraging a comprehensive comparative database. The robust and detailed benchmarks allowed the team to see where their organization stood compared to peers.

Here are just three examples of how the team incorporated performance benchmarking to fuel its recommendations:

#1  Reducing above-average telemetry usage

The group proactively found that telemetry usage at one of the system’s hospitals was 70 percent higher than the comparison group median. The PUSH team recommended a review of the hospital’s electronic medical record order set and staffing ratios, which led to changes. The hospital’s telemetry utilization was reduced to the comparison group median within three months.

#2  Increasing oncology patient volume

The health system had recently converted one of its hospitals into an outpatient facility offering emergency department (ED) and oncology services. Using comparison data, the team discovered an imbalance between oncology patient levels and staffing levels. Leadership acted quickly and launched a campaign to increase oncology patient and provider volumes to align with staffing levels.

#3  Boosting ED capacity

The chief financial officer of an IU Health hospital asked the PUSH group for guidance to figure out why ED visits were declining even though staff reported full capacity. The data showed the hospital was efficiently using its available space — treating 20 more ED patients per day than the comparison peer average — but it had six fewer treatment spaces. Based on that insight, the hospital received approval to expand to accommodate 25 more visits per day.

If you’d like more information on how the health system achieved these remarkable results, please reach out to us

 


Measuring the potential impact of a new palliative care service line


By Truven Staff/Monday, October 16, 2017

When you discover your mortality rates for stroke, pneumonia and heart failure aren’t where they need to be, what do you do?

The quality team at Carson Tahoe Health, a health system based in northern Nevada and eastern California, recently faced that challenge — and knew they had to answer two key questions: why is this happening and what can we do about it?

After a concentrated chart review, the health system discovered that the majority of patients who died in their care were at the end stages of their diseases.

Recent studies had pointed to the implementation of palliative care as a way to improve patient care and lower mortality rates. So to test the theory in their own environment, Carson Tahoe Health decided to roll out an inpatient care protocol in which hospitalists refer patients with end-of-life issues to a palliative care physician.

Comparing outcomes to determine progress

Using a clinical performance monitoring and benchmarking solution, the quality team was able to analyze several metrics, focusing on heart failure and chronic obstructive pulmonary disease (COPD) diagnosis-related groups in end stages of the diseases.

They tracked two groups of patients: those who participated in the palliative care protocol and those who did not. That way, cost, utilization, readmission, length of stay and other comparisons could be made.

The health system gained some key insights on the value of palliative care:

To see if they could move the dial even further on care improvement, the health system built and analyzed population reports to review the potential return on investment for an outpatient palliative program.

Informing the next step

With the new data and reports in hand, the health system proposed an outpatient palliative care service line to its board of directors, and the board approved it. Now Carson Tahoe Health offers a palliative care/heart failure chronic disease management clinic that sees patients within five days of discharge.

Seeing progress

After implementing the palliative care initiatives, the health system’s 30-day inpatient mortality rates for acute myocardial infarction, heart failure, COPD, pneumonia, stroke and coronary artery bypass graph surgery began trending lower than the national average.

They plan to expand palliative care services to other chronic end-stage disease groupings in the future.

If you’d like more information on how the health system achieved these results, please reach out to usYou can also read the full case study here.


How one health system closed care gaps and achieved improved ACO scores


By Truven Staff/Wednesday, September 27, 2017

As accountable care organizations (ACOs) for the Medicare Shared Savings Program (MSSP) know all too well, just one instance of a patient missing a follow-up visit to a clinic can change that person’s risk score. And those scores are ultimately important for demonstrating the MSSP goals of increasing quality of care while decreasing costs.

Cincinnati-based Mercy Health Select faced that challenge head on. Already one of the top ACOs in the US, Mercy Health Select wanted to do even more to close gaps in care and potentially improve the system’s ACO scores. Better scores would mean better care for patients and could help the system earn greater shared savings from the MSSP affiliation, too.

First, tackling a common issue: Different EHR systems

The Mercy Health organization started with a typical challenge for ACOs: Not all of the affiliated physicians in the network used the same electronic health record (EHR) system. That made it difficult to quickly consolidate information about at-risk patients across facilities.

That’s why Mercy Health Select opted to leverage technology that allowed the system to combine and mine the disparate sources of both clinical and claims data. The platform they chose also ran analytics on the data — to identify and prioritize the most at-risk, high-cost patients for follow-up communications.

Then, solving for speed

Of course, the timeframe for producing this kind of information is critical, too. If care coordinators wait too long to reach out to an at-risk patient who has not had a recommended cancer screening, for example, the ability to impact the individual’s health decreases.

So Mercy Health Select again used technology to update EHRs and patient risk scores within 24 hours. The new structure means clinicians can click on a link in a patient’s chart and see gaps in care right away — fueling fast intervention when necessary.

Solidifying the connection

Implementation of the new approach is paying off for Mercy’s ACO. With more gaps in care closed and more at-risk patients likely on the road to better health, the organization boosted its ACO score to 97.1 percent. That score is nearly 6 percent higher than the average1, and something all ACOs may aspire to achieve. After all, with that kind of improvement in score and patient care, ACOs are setting themselves up to earn greater shared savings.

If you’d like more information on how the health system achieved this result, please reach out to usYou can also read the full case study here


1 Muhlestein D, McClellan M, Saunders R. (2016, September 9) Medicare Accountable Care Organization Results For 2015: The Journey to Better Quality and Lower Costs Continues [Blog Post], retrieved from http://healthaffairs.org/blog/2016/09/09/medicare-accountable-care-organization-results-for-2015-the-journey-to-better-quality-and-lower-costs-continues/

 


Risk of Hospitalization


By Therese Gorski/Tuesday, December 13, 2016

As Anne Fischer wrote in her last blog How a Data Scientist Thinks about Risk Stratification, in order to predict risk, we need to first determine what “risk” is being measured. One important risk is that of being hospitalized.  Risk of hospitalization or admission models have become more targeted, more personal and seemingly more prevalent. Further, they are very much in line with the goals of population health and the “quadruple aim” (improving patient care, reducing costs, improving the health of populations and improving the provider’s experience). If a person has proper ambulatory or outpatient care for a chronic disease, acute inpatient admissions related to that disease should be rare.

At Truven Health, we have been developing and maintaining risk models for decades. Many risk models, such as risk of cost, risk of mortality and risk of complications, are best used in aggregate within a population subset. This evaluation is typically done at a service line or patient group level, for example, all cardiovascular patients or all patients with a specific MS-DRG.

In today’s world of increasing interest in care management and targeted outreach, individual level risk models hold much promise.  These models evaluate and interpret risk for each individual patient. Several years ago, we started a journey to develop new targeted risk models – including risk of hospitalization – to meet this increasing business need. We focused on specific diseases with the intent to have high predictive accuracy overall but particularly at the “tails”, meaning we must perform well at identifying those who have a high likelihood of being admitted in the near future. Or, in data science terms, model performance was measured by its sensitivity, specificity and positive predictive value for all patients above a specific risk threshold, with an emphasis on high sensitivity (that is, reducing false negatives).

To start, we chose to focus on diabetes, congestive heart failure (CHF), asthma, and more recently chronic obstructive pulmonary disease (COPD). These conditions run the gamut for both prevalence and admission risk, with asthma being most prevalent and least likely to result in admission versus CHF, which is least common but most likely to result in admission. These are also chronic conditions that are typically managed, at some level, which also fit our criteria.

In addition to specific diseases, we also focused on identifying risk at one-, three- and six-month intervals so that a care manager can better understand the risk at hand and be able to prioritize accordingly. Further, we report risk across several categories including “all-cause admissions”, “potentially avoidable admissions” (largely defined by AHRQ) and risk of “related admissions” which represents conditions that are considered to be related to the main condition as defined by Truven Health. Finally, along with each model’s risk score, we provide the patient attributes that are driving the risk score, whether it be a recent hospitalization, level of disease severity or even age. We believe that this additional insight gives the care manager a bit more background on the patient, helping to explain why the patient may have an increased risk value.

These models have been notably successful in terms of their predictive accuracy and our work will continue as we expand the number of diseases and work with our clients to help make the information actionable. The general trend toward person specific risk versus risk in aggregate will only grow and will become more refined as we, and the industry in general, are able to obtain and incorporate more personalized information about people.

Therese Gorski
Senior Director, Advanced Analytics


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