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Case Study: Management Information System at Dell

Management information system involves the information system and the organization. MIS begins where computer science ends. Computer scientists deserve accolades for developing and delivering even more advanced forms of information technology: hardware technology; software technology; and network technology. Yet because no technology implements itself, there is more to MIS than just information technology. MIS has dimensions. The four interrelated dimensions of MIS are as follows: First, MIS involves not just information technology, but also its instantiation; second, MIS involves, as reactive and inextricable elements, both an information system and its organizational context; third, MIS involves information technology as a form of intellectual technology; and fourth, MIS involves the activities of a profession or corporate function which are integral to the essence of what MIS is (Currie & Galliers, 1999).

Dell Computer Corporation: Company Background

Dell Computer Corporation is a major manufacturer of personal computers, computer peripherals, and software. Among the leading producers of computers in the world, Dell sells its products directly to customers through the Internet and mail-order catalogs rather than through retail outlets. The company is based in Round Rock, Texas. At Dell Computers, customers are brought into the product planning and manufacturing processes, with all employees encouraged having contact with customers. Through effective collaboration across boundaries, ideas can be shared about product designs and value propositions. The result is faster and more customer-focused product and service innovation. To produce the capacity for this, considerable attention must be placed on organizational structures, processes, skills and culture. Such elements may need a radical overhaul in established companies (Dennis & Harris, 2002). Dell was founded in 1984 by Michael Dell. In 1983, during his freshman year at the University of Texas, he bought excess inventory of RAM chips and disk drives for IBM personal computers from local dealers. He resold the components through newspaper advertisements at prices far below retail cost. By 1984, his sales totaled about $80,000 a month. In April 1984, Dell dropped out of school to launch his company (Ford, Honeycutt, & Simintiras, 2003).

The new company soon began manufacturing its own IBM-compatible computers under the name PCs Limited. Because Dell sold computers directly to users through advertisements in magazines and catalogs, the company could price its machines lower than those sold through retail stores. Sales reached nearly $6 million during the company’s first year, climbing to $34 million the following year. By 1987, Dell was the leading mail-order computer company in the United States. In that year, it created a sales force to target large corporations and began adding international offices to capture the direct-mail market outside the United States (Ford, Honeycutt, & Simintiras, 2003). While the company continued to grow rapidly; Dell experienced a series of setbacks that hurt profits. In 1990, the company began selling computers through retail stores, an effort it abandoned in 1994. In 1991, Dell launched a line of notebook computers, but quality problems and inadequate production planning forced the company to stop selling for a year. In 1994, Dell launched a new line of notebook computers and expanded efforts to increase overseas sales. Dell also began focusing on the market for servers, which used the computers to run local area networks. By the late 1990s, Dell was firmly in place as the world’s number one direct seller of computers. More than 50 percent of the company’s computer sales transactions took place via its website, which generated worldwide sales in excess of $40 million a day (Ford, Honeycutt, & Simintiras, 2003).

Information Processing Tools

Information processing or Data processing is the analysis and organization of data. It is used extensively in business, engineering, and science and an increasing extent in nearly all areas in which computers are used. Businesses use data processing for such tasks as payroll preparation, accounting, record keeping, inventory control, sales analysis, and the processing of bank and credit card account statements. Engineers and scientists use data processing for a wide variety of applications, including the processing of seismic data for oil and mineral exploration, the analysis of new product designs, the processing of satellite imagery, and the analysis of data from scientific experiments (Thierauf, 1978).

Data processing is used extensively in business, engineering, and science and to an increasing extent in nearly all areas in which computers are used. Data processing is divided into two kinds of processing: database processing and transaction processing. A database is a collection of common records that can be searched, accessed, and modified, such as bank account records, school transcripts, and income tax data. In database processing, a computerized database is used as the central source of reference data for the computations. Transaction processing refers to interaction between two computers in which one computer initiates a transaction and another computer provides the first with the data or computation required for that function. Most modern data processing uses one or more databases at one or more central sites (Thierauf, 1978).

Transaction processing is used to access and update the databases when users need to immediately view or add information; other data processing programs are used at regular intervals to provide summary reports of activity and database status. Examples of systems that involve all of these functions are automated teller machines, credit sales terminals, and airline reservation systems (Thierauf, 1978).

The information processing tools that Dell uses include computers, the internet, maps, spreadsheets, models, and databases. For the operational level of Dell, the most appropriate tool for information processing is maps. Through the said information processing tool, decisions on how to operate the organization can be initialized and made. Maps can be used to determine which country/place information will be acquired from, it can also assist in determining the demographic level of people and information will be gathered . Maps can be in the form of charts that can also provide necessary information. The information gathered in turn can assist in helping to decide how an organization will be operated. For the tactical level of Dell, the most appropriate tool for information processing is databases. Through the said information processing tool, the records that can assist in finding out the strength and weakness of the company can be used to determine the tactic that will be used by the organization. For the strategic level of Dell, the most appropriate information processing tool is the internet or World Wide Web. Through the internet, trends and strategies by other companies can be known. After analyzing the trends and strategies used by other companies, an appropriate strategy can be formulated to use by the organization.

Inventory control systems

Individual businesses need, first and foremost, an efficient inventory control system. This implies the minimum amount of inventory that will provide the consumers with what they need whenever and wherever they need it. Effectiveness of the inventory system means basically having an inventory mix that is most likely successful in satisfying consumer needs (Samli & Sirgy, 1995). The inventory control systems used by Dell is up to date and reliable to prevent problems to arise. The inventory system of Dell makes sure that anything the consumer need will be available to them at any given time. It is also what the company uses to know if certain products are still available or misuse of the inventory system may cost problems to the company.

Management information system involves the information system and the organization. Dell benefits a lot from the management information system. The system helps the company create strategies that will help the company conquer any problems and threats from competitors. The system also assists the company in processing the needed information. Management Information Systems also helps a company to create or update its inventory control system.

Recommendations

Since the MIS of a company is a vital part of its operations and its survival in the modern world, it must be well updated and it must compete well with MIS’s competitors. The MIS of a company should be created from high standards so that it can be of stiff competition against its counterparts. The MIS system should help the company to achieve its goals and assist the company in reaching its potential.

1. Comment on the MIS in Dell and suggest the positives and negatives of MIS in Dell?

2. The dell directly sells its computers to the customer whether it will give them good and reliable information or they are lacking in information system due to this move?

3. Develop the information flow diagram for dell and suggest some improvement in the same.

4. MIS is a combination of Management, Information and System otr of the three parts of the information system in which area does the Dell lacking?

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3 thoughts on “ Case Study: Management Information System at Dell ”

please publish the answers for the questions also… so that we can use for reference

Please post answers to the questions also so that we can also use it for our reference. Thanks.

Is it possible to have the answers to be used for reference please ?

Case Study: How we created a Management Information System (MIS) for a UK-based financial company

It’s no secret that making the right decisions based on consistent and clear data is key for the success of any company. However, dealing with tons of monthly and yearly reports makes data visualization and decision-making strenuous tasks that result in poor financial and operational inefficiency. With that in mind, more and more companies put in place management information systems to turn tons of data into useful information. Not only does it help them to facilitate the flow of information, but they can also avoid wrong choices.

What exactly is a Management Information System?

Generally, management information systems are used to coordinate, control and visualize data needed by a company's managers, business owners and other decision-makers to make informed and data-driven decisions for the company. Not only does it help businesses and companies operate more effectively, but it also allows them to better serve their customers, staff and bottom line. Let’s overview key functions of management information systems (MISs):

Data capturing
Data storage
Data processing
Data and information distribution
Prediction/forecasting

Why do you need a Management Information System for your business?

Let’s discover why companies should take into account management information solutions:

If you need to identify a company's strengths and weaknesses
If you need to get quick access to massive amounts of data
If you need to help middle managers optimize decision-making and monitor performance
If you need to promote better communications and connectivity between departments and share the same information
If you need to eliminate time spent on gathering the data
If you need to improve customer service and create more effective marketing and promotional campaigns.

What’s the idea of the project?

DDI Development team has great experience in creating and implementing an ocean of software solutions to meet business needs in such verticals as logistics, healthcare, eCommerce, eLearning, recruiting, fintech, and booking. Recently, we developed and implemented a management information system (MIS) for a UK-based financial company. Not only does it help our client identify non-performing areas in business, but our client can make more rational decisions based on reliable data. Only by incorporating the management information system can our client do the following:

Get accurate information faster
Spot problem areas and find agreeable solutions
Make crucial business decisions with ease
Improve operational flow

The development process of the management information system

Here we are going to overview the development process of the management information system (MIS) for a UK-based company. Keep on reading!

To create and implement a management information system, we needed the following specialists involved in the process:

1 Business analyst (BA)
1 Project manager (PM)
2 UI/UX designers
3 Front-end engineers
2 Quality assurance engineers (QA)
2 Backend engineers

Methodology

When developing the management information solution, we focused on Agile methodology taking into account the following factors:

The development team could easily adapt to any changes if the client’s requirements changed.
The team could consider all the client’s needs over the requirements in the development plans.
Instead of established processes, the team could concentrate more on results that were more important for both the team and the client.

Thanks to the key components of agile-driven development, our development team built and incorporated a management information system into the client’s business within 24 sprints.

Development Process

For a quick start, the Business Analyst interviewed the client and stakeholders to gain a clear understanding of the problem they would like to tackle with a management information system (MIS). Once the Business Analyst collected all the essential information about the business processes, potential users, critical or necessary data, etc., the development team was able to define key features for the potential project and suggest necessary technology for their implementation. In addition to these, the Business Analyst created a project specification document outlining the desired system look and functional specification. Not only did it help the team understand how the client and stakeholders see the appearance and capabilities of the upcoming management information solution, but it could also provide more information for the development team.

Based on this information, the Project Manager could produce a preliminary estimate to the client and suggest the development process roadmap. Once it was confirmed, the planned scope of work (so-called Backlog) was broken down into 2-week sprints. Before each sprint, the whole team of designers, front-end and back-end engineers, QA engineers reviewed which features from the backlog they worked on and committed to some result by the end of each sprint. With demo sessions, the team gave feedback and discussed with the client any possible changes to the backlog which would help to maximize project value. Once the management information system (MIS) was free of bugs, the team launched it and collected feedback from users to make any improvement or upgrade.

There are two key user levels within the system: Financial Analyst and Administrator. However, you can create more user roles such as C-level board (CFO, CEO, etc.) if there is a necessity for your business.

Financial Analyst : a user who is responsible for looking at massive amounts of data to identify opportunities or evaluate outcomes for business decisions; growing financial performance through analysis of financial forecasts, results, etc.
Administrator : this role is assigned to a user who can manage every aspect of the management information system - from creating user roles to defining their access levels and permissions within the system.

Financial Analyst Dashboard

You can register in the system as a Financial Analyst and land on the main page - Dashboard. It’s the homepage of the management information system. On the Dashboard page, you can see the tabs including Activities, Processes, Tasks, Sales Metrics and Finance Metrics. The Sales metrics tab shows a user's, team's or company's performance and helps track progress toward goals, prepare for future growth, adjust sales compensation, identify any strategic issues, etc. The Finance Metrics tab represents information such as revenue, expenses, net income, cashflow and so on that illuminate a business’s performance and help identify issues holding it back.

With the Tasks feature, you can properly plan your tasks daily, weekly or even on a monthly basis. Not only does it simplify your work, but it also minimizes the chances of missing them out. Moreover, you can view who created the task, task deadline, estimated time, etc. In addition to this, you can also filter and search tasks or display your tasks into different layouts - List or Kanban.

The Activities feature allows you to visualize the relationships and dependencies between the various activities and tasks, view the timelines and deadlines of all tasks, etc. Not only does it help you provide visibility into team workloads, but it also makes sure the teams are on the same page. Moreover, it eliminates confusion about dependencies, especially which team needs to finish what before another team can begin their task.

Activities for Management Information System

Thanks to the Processes feature, you can discover all the processes that are absolutely essential to efficiently running a business. They are the following: Sales, Orders, Purchases, Invoices, Sales. The Sales process tab shows the sales pipeline and allows you to identify where in the sales funnel your deals are, which sales activities generate revenue, how many customers have been turned into closed deals.

The Reports feature provides you with an accurate and clear picture of a company’s finances, including their revenues, expenses, profits and cash flow, etc. Not only does it help you gain in-depth insights into financial information and streamline your business’s financial activities, but you can also create legally compliant reports, from budgets to cash flow statements and balance sheets by avoiding human errors. Here you can find the following types of reports:

Sales Reports
Financial Reports
Profit/Loss Reports
Income Reports
Purchase Reports
Market Reports

Reports in management information system

Notifications

Thanks to the Notifications feature, you can keep information under control and organized. Moreover, you ensure that you never miss an important update and are always on top of what’s going on in the areas of information you need the most. Not only do real-time notifications update you immediately to important changes or events, but you can also stay up-to-date.

Administrator Dashboard

Management information platform dashboard

Only by signing in as an Administrator can you land on the Dashboard page. Here you can find such options as Data sources, Integrations, Notifications and System Settings. Let’s take a closer look at them below!

Data sources

With the Data sources feature, you can discover where data is born, where it is digitalized, etc. Its main purpose is to capture or hold data of interest. Not only does it help applications or other systems connect to and move data to where it needs to be, but it also produces information in a more easily understood and user-friendly format.

Integrations

Thanks to the Integrations feature, you can connect a variety of additional third-party solutions with your system that will add your existing system efficiency or scalability or simplifies the workload. Only by using integration feature can business and work processes be done more effectively and efficiently.

System Settings

The System Settings feature represents a significant number of options for configuration and customization and allows you to configure a number of system functions and features with a simple click. On the System Settings page, you can find out such tabs as Users, Tasks, Processes, Reports and switch between them to make any changes.

What are the key components of Management Information System?

Generally, the management information systems (MISs) include the following components:

People Resources : users involved in the day-to-day business processes and required for the operation of all information systems.
Data Resources : means a database that gathers and processes data collected from activities.
Software Resources : means all specialized software required to perform a variety of tasks and activities within the company.
Hardware Resources : includes all physical devices such as computers, printers, networking devices to process information.
Process : includes all the business processes that are necessary to attain established goals.

Also, you can read about : How to create CRM for the banking sector

What are the benefits of Management Information System for your business?

The points below show the benefits you can derive from a good management Information system (MIS). Don’t hesitate to read!

Trends analysis : applying various algorithms, the system can analyze the current market trends and predict future trends based on such information to help stakeholders or business owners plan strategically and determine future goals.
Reduced risk of errors : the system eliminates data replication that minimizes chances of human errors being made and contributes to more accurate data available.
Better decision-making : with MIS solution in place, you can access accurate, timely, relevant and complete information on how well your business runs. Not only does it help you discover a complete picture of a situation, but it also allows you to make a data-driven decision to change the situation.
Problems identification : with the system, you can map out and repair any issue as efficiently as possible. Only by identifying the source of the problem and breaking down it into components can you uncover solutions to address it.
Increased efficiency : thanks to the MIS solution, you can manage every aspect of your business by accessing more complete and more recent information. Not only does it help you reduce the data management frustration, but your business can operate more efficiently.
Improved productivity : with the MIS solution, the overall work process runs more effectively in terms of access to the same information and better collaboration between departments and teams.

Bottom line: Ready to put Management Information System in place?

With the rapid development of a variety of software solutions, companies can remain competitive. Having a management information system on hand allows you to get a clear picture on management and business processes, strategy and performance in your company. Moreover, it provides a plethora of reports on all the data you have to make informed decisions. Only by understanding your business’ data can you plan strategically and allocate resources in an organized and systematic way. That’s why if you want to grow your business and survive in the market, a management information system (MIS) is what you need. Drop us a line if you want to turn thousands of data into helpful, usable and information.

Be aware of contemporary trends. Do not miss the discussion of professionals

10 Major Differences Between Android and iOS App Development

Management Information System for Effective and Efficient Decision Making: A Case Study

Decision making & management science ejournal-CBMO

20 Pages Posted: 11 Nov 2012 Last revised: 2 Nov 2021

Gautham Nayak

Affiliation not provided to ssrn, aloysius h. sequeira.

National Institute of Technology Karnataka (NITK), Surathkal

Sanjay Senapati

National Institute of Technology Karnataka (NITK), Surathkal

Date Written: November 11, 2012

Decision making is an integral part of the functioning of any organization. To facilitate decision making in this ever-competitive world it is imperative that managers have the right information at the right time to bridge the gap between need and expectation. To facilitate better flow of information adequate Management Information Systems (MIS) is the need of the hour. Thus it is important to have an understanding of the MIS followed in an organization by all levels of management in order to take effective decisions. A management information system collects and processes data (information) and provides it to managers at all levels who use it for decision making, planning, program implementation, and control. The MIS has many roles to perform like the decision support role, the performance monitoring role and the functional support role. To get a realistic and holistic view of the MIS, MIS of MCC Limited (name disguised) was taken as a case study. To get a more detailed understanding of a particular function of the company, we studied the need, uses and benefits of MIS with respect to the Material Department of the company. Inventory Management was of prime focus in our study. MCC Limited is one of the first Indian companies to realize the potential and importance of information technology and adopt automation and IT. The organization started computerizing its systems as early as 1968. The organization has traveled a long way from the days in 1968 when it was using simple keypunching machines. Significant improvements have been made in the application systems and infrastructure. From Batch processing to on-line systems, from IBM1401 to the latest UNIX and Windows 2003 based machines it has made timely transitions determined by available technologies and business requirements. The MIS has greatly facilitated and synchronized the information flow in the organization and the management feels that is has played a role in the growth and increased performance of the company.

Keywords: MIS, decision making

Suggested Citation: Suggested Citation

Aloysius Henry Sequeira (Contact Author)

National institute of technology karnataka (nitk), surathkal ( email ).

School of Management Surathkal Mangalore, Karnataka 575025 India 0824 2474000 (Phone)

HOME PAGE: http://som.nitk.ac.in/faculty/aloysius-henry-sequeira

National Institute of Technology Karnataka (NITK), Surathkal ( email )

Surathkal Mangalore, KARNATAKA 575025 India

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A scientific methodology for MIS case studies

Northeastern Univ., Boston, MA

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A methodology for conducting the case study of a management information system (MIS) is presented. Suitable for the study of a single case, the methodology also satisfies the standard of the natural science model of scientific research.This article provides an overview of the methodological problems involved in the study of a single case, describes scientific method, presents an elucidation of how a previously published MIS case study captures the major features of scientific method, responds to the problems involved in the study of a single case, and summarizes what a scientific methodology for MIS case studies does, and does not, involve.The article also has ramifications that go beyond matters of MIS case studies alone. For MIS researchers, the article might prove interesting for addressing such fundamental issues as whether MIS research must be mathematical, statistical, or quantitative in order to be called "scientific". For MIS practitioners, the article's view of scientific method might prove interesting for empowering them to identify, for themselves, the pint at which scientific rigor is achieved in an MIS research effort, and beyond which further rigor can be called into question, especially if pursued at the expense of professional relevance.

Index Terms

Applied computing

Operations research

Decision analysis

Information systems

Information systems applications

Decision support systems

Social and professional topics

Professional topics

Computing and business

Management of computing and information systems

Project and people management

Project management techniques

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The MIS case study page

These case studies are provided for students who are studying the MIS course at the University of York. You can gain access to the case studies from the links below. If you are not a student on the MIS course, you are welcome to use them for teaching and non-profit use. If you wish use these case studies, please contact me first - [email protected] .

The interviews used in these case studies are edited and anonymous versions of the interviews used in C. KIMBLE and K. McLOUGHLIN. - Computer Based Information Systems and Managers Work . New Technology, Work and Employment, 10(1), March, 1995, pp 56 - 67. ISSN 0268-1072. Further details of this work can be found here .

If you wish to research the issues raised in these case studies further, you may find the links on the MIS links page or some text books from the MIS books section of some use to you.

All of the case studies have the same form. Each contains:

Some background material on the organization and systems that form the basis of the case study
Transcribed interviews with managers and/or users of the system.

The case studies are intended to be group exercises. You should:

Form teams of the appropriate size.
Each individual team member should read the background material and one case study interview.
The whole team should meet together and answer the questions.

Case Study One (teams of five)

Case Study One is the smallest of the case studies (25 pages) and looks at the problems that a chemical company (Company Y) experienced when it attempted to implement a particular type of information system (MRP II) in its plants. The case study is based on five interviews with directors and senior managers in the company. You should note that each interview only contains part of the whole picture and are spread over a period of 18 months.

Case Study Two (teams of six)

Case study two is larger (67 pages) and is split into two documents: the background (22 pages) and the interviews (45 pages). The case study is about (a) the effects that the introduction of the Customer Service System (CSS) had on the way that BT were able to manage their business and (b) the way in which CSS affected the way in which those managers worked. The background material consists of general background material on BT and some press cuttings on BT. The interviews are edited transcripts from interviews with six managers at BT. Again, each interview only contains part of the whole picture although in this case all of the interviews were conducted at the same time.

Case Study Three (teams of eight)

The final case study is larger again (72 pages) and is also split into two documents: the background (18 pages) and the interviews (53 pages). The case study is about the effects of the introduction of a network (LAN) and messaging system (e-mail) into the Central Post Office in Newcastle upon Tyne. The background material consists of general background material on Royal Mail and some press cuttings. There are eight interviews all of which were conducted at the same time. As before, each interview contains only part of the whole picture.

DOI: 10.17705/1cais.01116
Corpus ID: 26870153

Management Information Systems Research: What's There in a Methodology?

Prashant C. Palvia , E. Mao , +1 author Khalid S. Soliman
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Business, Computer Science

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A profile of information systems research published in information & management, exploring the management information systems discipline: a scientometric study of icis, pacis and asac, research directions in information systems field, current status and future trends: a literature analysis of ais basket of top journals, a meta-analysis of current global information systems research, research models in information systems, the state of research on information systems success, understanding acceptance of information system development and management methodologies by actual users: a review and assessment of existing literature, historical development of research methods in the information systems discipline, information systems executives: a review and research agenda, scientometrics-based study of computer science and information systems research community macro level profiles, 33 references, combining qualitative and quantitative methods in information systems research: a case study, survey research methodology in management information systems: an assessment, research in information systems: an empirical study of diversity in the discipline and its journals, research in management information systems, 1980-1984: points of work and reference, revisiting dss implementation research: a meta-analysis of the literature and suggestions for researchers, a framework for research in computer-based management information systems, a scientific methodology for mis case studies.

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The success of a management information system in health care - a case study from Finland

Affiliation.

1 Central Finland Health Care District, Keskussairaalantie 19, FI-40620 Jyväskylä, Finland. [email protected]
PMID: 22705086
DOI: 10.1016/j.ijmedinf.2012.05.007

Purpose: The purpose of this article is to describe perspectives on information availability and information use among users of a management information system in one specialized health care organization. The management information system (MIS) is defined as the information system that provides management with information about financial and operational aspects of hospital management.

Methods: The material for this qualitative case study was gathered by semi-structured interviews. The interviewees were purposefully selected from one specialized health care organization. The organization has developed its management information system in recent years. Altogether 13 front-line, middle and top-level managers were interviewed. The two themes discussed were information availability and information use. The data were analyzed using inductive content analysis using ATLAS.ti computer program.

Results: The main category "usage of management information system" consisted of four sub-categories: (1) system quality, (2) information quality, (3) use and user satisfaction and (4) development of information culture.

Conclusions: There were many organizational and cultural aspects which influence the use of MIS in addition to factors concerning system usability and users. The connection between information culture and information use was recognized and the managers proposed numerous ways to increase the use of information in management work. The implementation and use of management information system did not seem to be planned as an essential tool in strategic information management in the health care organization studied.

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Yaser Hasan Salem Al-Mamary د. ياسر حسن المعمري

For the last twenty years, different kinds of information systems are developed for different purposes, depending on the need of the business . In today’s business world, there are varieties of information systems such as transaction processing systems (TPS), office automation systems (OAS), management information systems (MIS), decision support system (DSS), and executive information systems (EIS), Expert System (ES) etc . Each plays a different role in organizational hierarchy and management operations. This study attempts to explain the role of each type of information systems in business organizations.

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The corporate competitive environment is being liberalized and globalized, therefore the organizations, especially dry food packaging industry, need greater interaction between their stakeholders. One of the problems organizations face to is the segregation of the business functions in an organization. Thus, the business experienced to implement ERP systems for solving this problem. In contrast, the ERP projects have not been effective enough and hence have been unable to achieve all the results envisaged. Therefore, an in depth understanding about the benefits of ERP implementation is needed to ensure the successful system implementation. This study seeks to determine and classify the benefits of ERP system implementation in dry food packaging industry. The methodology of this research comprised of three phases: define the benefits of ERP system implementation from the current literatures, categorize them into strategic, tactical and operational benefits in each business functions in an organization. These ERP benefits are summarized in this paper as a research finding to assist the managers in implementing ERP system successfully.

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A Scientific Methodology for MIS Case Studies

Author	Allen S. Lee
Year	1989
Volume	13
Issue	1
Keywords	Information systems, case studies, research methods, research design, organizational impacts
Page Numbers	33-50

Introduction
Conclusions
Article Information

a The Centers for Medicare and Medicaid Services (CMS) issued a broad request for applications to participate in the Million Hearts Model. To participate, organizations had to submit a complete application, have at least 1 practitioner enrolled in and eligible to bill for Medicare Part B, use an electronic health record system certified by the Office of the National Coordinator for Health Information Technology, and have met the criteria for the CMS electronic health record Incentive Programs, also known as Meaningful Use, in performance year 2014. Five organizations were ineligible because they failed to meet 1 or more of these criteria. CMS rejected an additional 6 organizations for ambiguous or unsatisfactory responses to application questions about these same criteria or about willingness or ability to participate in the model.

b Assignment occurred at the organization level. CMS used a minimization procedure (adaptive randomization), equivalent to random assignment (eMethods 1 in Supplement 2 ).

c In the control group, the number of participating practitioners per organization was capped at 20. This resulted in fewer beneficiaries per organization, on average, in the control group vs the intervention group.

d Organizations were implicitly excluded from the analysis population if none of their model beneficiaries met the inclusion criteria.

e Beneficiaries had to be aged 40 to 79 years when entering the model with no prior acute myocardial infarction, prior stroke, kidney failure, or hospice use.

f All beneficiaries remained alive and in Medicare fee-for-service at least 1 day after entering the model, although censoring sometimes occurred later due to beneficiaries dying or exiting Medicare fee-for-service.

a The cumulative probabilities are modeled based on Cox proportional-hazards regressions of intervention and control group beneficiaries. The intervention group line represents the percentage of intervention group beneficiaries estimated to experience an event during the model. The control group line represents the percentage of beneficiaries who would experience an event over the same period, if those beneficiaries had the same characteristics (covariates) as intervention group beneficiaries but did not experience the Million Hearts Model.

Trial protocol and statistical analysis plan

Data sharing statement

Million Hearts 2022—Steps Needed for Cardiovascular Disease Prevention JAMA Viewpoint November 13, 2018 This Viewpoint describes the Million Hearts initiative, a US Centers for Disease Control and Prevention (CDC) and Centers for Medicare & Medicaid Services program aimed at preventing 1 million acute cardiovascular (CV) events by improving key cardiovascular disease (CVD) risk factors, and summarizes two 2018 CDC reports describing the burden of preventable CVD deaths and the slow rate of improving population-level measures of CV health. Janet S. Wright, MD; Hilary K. Wall, MPH; Matthew D. Ritchey, PT, DPT, OCS, MPH
Million Hearts Cardiovascular Disease Risk Reduction Model JAMA Editorial October 17, 2023 Gabriel S. Tajeu, DrPH, MPH; Karen Joynt Maddox, MD, MPH; LaPrincess C. Brewer, MD, MPH
Effect of the Million Hearts Cardiovascular Disease Risk Reduction Model on Initiating and Intensifying Medications JAMA Cardiology Original Investigation September 1, 2021 This prespecified secondary analysis of a randomized clinical trial assesses whether the Million Hearts Cardiovascular Disease Risk Reduction Model increased the initiation or intensification of antihypertensive medications or statins among patients with blood pressure or low-density lipoprotein cholesterol levels above guideline thresholds for treatment intensification. G. Greg Peterson, PhD, MPA; Jia Pu, PhD, MA; David J. Magid, MD, MPH; Linda Barterian, MPP, MPH; Keith Kranker, PhD; Michael Barna, MA; Leslie Conwell, PhD, MHS; Adam Rose, MD, MSc; Laura Blue, PhD, MA; Amanda Markovitz, ScD, MPH; Nancy McCall, ScD, SM; Patricia Markovich, PhD

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Blue L , Kranker K , Markovitz AR, et al. Effects of the Million Hearts Model on Myocardial Infarctions, Strokes, and Medicare Spending : A Randomized Clinical Trial . JAMA. 2023;330(15):1437–1447. doi:10.1001/jama.2023.19597

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Effects of the Million Hearts Model on Myocardial Infarctions, Strokes, and Medicare Spending : A Randomized Clinical Trial

1 Mathematica, Washington, DC
2 Mathematica, Cambridge, Massachusetts
3 Department of Medicine, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, Pennsylvania
4 RAND Corporation, Santa Monica, California
5 Mathematica, Oakland, California
6 University of Colorado School of Medicine, Denver
7 Mathematica, Chicago, Illinois
8 RAND Corporation, Arlington, Virginia
9 Center for Medicare and Medicaid Innovation, Centers for Medicare & Medicaid Services, Baltimore, Maryland
Editorial Million Hearts Cardiovascular Disease Risk Reduction Model Gabriel S. Tajeu, DrPH, MPH; Karen Joynt Maddox, MD, MPH; LaPrincess C. Brewer, MD, MPH JAMA
Viewpoint Million Hearts 2022—Steps Needed for Cardiovascular Disease Prevention Janet S. Wright, MD; Hilary K. Wall, MPH; Matthew D. Ritchey, PT, DPT, OCS, MPH JAMA
Original Investigation Effect of the Million Hearts Cardiovascular Disease Risk Reduction Model on Initiating and Intensifying Medications G. Greg Peterson, PhD, MPA; Jia Pu, PhD, MA; David J. Magid, MD, MPH; Linda Barterian, MPP, MPH; Keith Kranker, PhD; Michael Barna, MA; Leslie Conwell, PhD, MHS; Adam Rose, MD, MSc; Laura Blue, PhD, MA; Amanda Markovitz, ScD, MPH; Nancy McCall, ScD, SM; Patricia Markovich, PhD JAMA Cardiology

Question Did the Million Hearts Model, which encouraged and paid for cardiovascular risk assessment and reduction, reduce the incidence of first-time myocardial infarctions and strokes or Medicare spending among Medicare beneficiaries aged 40 to 79 years?

Findings The model reduced the probability of a first-time myocardial infarction or stroke over 5 years by 0.3 percentage points among people at high or medium risk for these events, without statistically significant changes in Medicare spending.

Meaning The commitment of health care organizations to cardiovascular risk assessment and follow-up, coupled with payments for risk assessment and reduction, reduced myocardial infarction and stroke rates. Results support guideline recommendations for cardiovascular risk assessment.

Importance The Million Hearts Model paid health care organizations to assess and reduce cardiovascular disease (CVD) risk. Model effects on long-term outcomes are unknown.

Objective To estimate model effects on first-time myocardial infarctions (MIs) and strokes and Medicare spending over a period up to 5 years.

Design, Setting, and Participants This pragmatic cluster-randomized trial ran from 2017 to 2021, with organizations assigned to a model intervention group or standard care control group. Randomized organizations included 516 US-based primary care and specialty practices, health centers, and hospital-based outpatient clinics participating voluntarily. Of these organizations, 342 entered patients into the study population, which included Medicare fee-for-service beneficiaries aged 40 to 79 years with no previous MI or stroke and with high or medium CVD risk (a 10-year predicted probability of MI or stroke [ie, CVD risk score] ≥15%) in 2017-2018.

Intervention Organizations agreed to perform guideline-concordant care, including routine CVD risk assessment and cardiovascular care management for high-risk patients. The Centers for Medicare & Medicaid Services paid organizations to calculate CVD risk scores for Medicare fee-for-service beneficiaries. CMS further rewarded organizations for reducing risk among high-risk beneficiaries (CVD risk score ≥30%).

Main Outcomes and Measures Outcomes included first-time CVD events (MIs, strokes, and transient ischemic attacks) identified in Medicare claims, combined first-time CVD events from claims and CVD deaths (coronary heart disease or cerebrovascular disease deaths) identified using the National Death Index, and Medicare Parts A and B spending for CVD events and overall. Outcomes were measured through 2021.

Results High- and medium-risk model intervention beneficiaries (n = 130 578) and standard care control beneficiaries (n = 88 286) were similar in age (median age, 72-73 y), sex (58%-59% men), race (7%-8% Black), and baseline CVD risk score (median, 24%). The probability of a first-time CVD event within 5 years was 0.3 percentage points lower for intervention beneficiaries than control beneficiaries (3.3% relative effect; adjusted hazard ratio [HR], 0.97 [90% CI, 0.93-1.00]; P = .09). The 5-year probability of combined first-time CVD events and CVD deaths was 0.4 percentage points lower in the intervention group (4.2% relative effect; HR, 0.96 [90% CI, 0.93-0.99]; P = .02). Medicare spending for CVD events was similar between the groups (effect estimate, −$1.83 per beneficiary per month [90% CI, −$3.97 to −$0.30]; P = .16), as was overall Medicare spending including model payments (effect estimate, $2.11 per beneficiary per month [90% CI, −$16.66 to $20.89]; P = .85).

Conclusions and Relevance The Million Hearts Model, which encouraged and paid for CVD risk assessment and reduction, reduced first-time MIs and strokes. Results support guidelines to use risk scores for CVD primary prevention.

Trial Registration ClinicalTrials.gov Identifier: NCT04047147

In 2017, the US Centers for Medicare & Medicaid Services (CMS) launched the Million Hearts Cardiovascular Disease (CVD) Risk Reduction Model. Under this model (a health care delivery initiative tested by the CMS Innovation Center), CMS paid participating health care organizations to assess and reduce CVD risk among Medicare fee-for-service beneficiaries aged 40 to 79 years and organizations agreed to follow guideline-concordant care processes for the primary prevention of CVD. The model was unique in paying for overall CVD risk reduction, measured by a novel, longitudinal risk calculator, 1 rather than tying performance-based payments to control of individual risk factors. 2 CMS tested the model from 2017 to 2021 in a cluster-randomized, pragmatic trial including hundreds of thousands of beneficiaries across the US.

CVD remains the leading cause of death in the US and progress has stalled in recent years, 3 with substantial room to improve modifiable risk factors. 4 The Million Hearts Model, part of the US government’s broader Million Hearts initiative, 5 aimed to reduce first-time myocardial infarctions (MIs) and strokes by 7%. CMS hypothesized that this 7% relative effect could reduce Medicare spending enough to offset model payments, making the model cost-neutral.

A previous study 6 showed the Million Hearts Model increased practitioners’ use of CVD risk assessment. In addition, Medicare beneficiaries in the intervention group were more than 10% more likely than control beneficiaries to appropriately initiate or intensify antihypertensive or statin medications within a year of entering the model. Over the same period, systolic blood pressure and low-density lipoprotein cholesterol among high-risk beneficiaries declined 1% to 2% relative to the control group. The current study extended this work, examining model effects on long-term outcomes (measured over a maximum of 5 years) including first-time CVD events (MIs, strokes, and transient ischemic attacks [TIAs]) and Medicare spending.

The trial protocol is available in Supplement 1 . This study was part of a mixed-methods evaluation. 7 Beneficiaries were tracked as part of the group to which their organization was randomized following intention-to-treat principles. The study was registered at ClinicalTrials.gov (NCT04047147) and was exempt from federal policies on human subjects research, including institutional review board review, as an evaluation of public benefits or services. 8

The study ran from January 3, 2017, to December 31, 2021, and had 2 main components. First, intervention organizations signed a model participation agreement in which they agreed to perform CVD preventive care aligned with guidelines from the American College of Cardiology and the American Heart Association. 9 As part of this agreement, intervention organizations agreed to calculate Medicare beneficiaries’ risk of MI or stroke over 10 years (a CVD risk score). Risk scores relied on demographic information (age, sex, and race [because Black race is associated with higher CVD-event risk in the US 10 ]), clinical information (blood pressure, lipid levels, and diabetes status), smoking status, and antihypertensive use. 11 Organizations varied in how they implemented the risk score calculation, with most either building a risk calculator into the electronic health record or having staff calculate risk scores using an existing web- or phone-based app. The organizations further agreed to provide cardiovascular management services to high-risk beneficiaries (CVD risk score ≥30%). This included (1) discussing CVD risk with patients; (2) developing individualized care plans with patients for reducing risk; (3) annual in-person reassessment visits, including recalculating 10-year CVD risk scores using a longitudinal calculator designed for this purpose 1 ; and (4) additional contact at least twice annually to monitor progress.

Second, CMS offered payments to organizations to assess and reduce CVD risk. From 2017 to 2021, CMS provided intervention organizations 1-time payments of $10 per eligible beneficiary for whom they assessed risk. Further, in 2017 only, CMS paid intervention organizations $10 per beneficiary per month (PBPM) for each high-risk beneficiary to support cardiovascular care management. Then, from 2018 to 2021, CMS made performance-based risk-reduction payments: CMS paid each organization $0, $5, or $10 PBPM for each high-risk beneficiary with an annual risk reassessment, with monthly payment amounts dependent on mean risk score change across all of the organization’s high-risk beneficiaries reassessed. Beneficiaries’ risk score change was measured each year relative to the baseline score.

In addition, CMS created the Million Hearts Data Registry, through which organizations submitted clinical data needed to calculate risk scores initially and at reassessments. CMS provided webinars and peer-to-peer learning sessions and sent each organization semiannual performance reports.

CMS paid standard care control organizations $20 to submit the clinical data needed to calculate risk scores via the Million Hearts Data Registry (for the initial visits and each annual visit thereafter), with no payments PBPM. CMS did not ask control organizations to calculate risk scores, but staff at organizations randomized to the control group could have used their data to calculate risk scores and followed guidelines for CVD prevention.

Randomization occurred at the organization level. CMS used a minimization procedure (adaptive randomization) equivalent to random assignment 12 - 14 (eMethods 1 in Supplement 2 ).

Organizations volunteered for participation following a CMS request for applications. Eligible organizations had to have an electronic health record and at least 1 practitioner (physician, nurse practitioner, or physician assistant) billing Medicare.

Medicare fee-for-service beneficiaries entered the model during the intervention period (after randomization) when they visited a participating practitioner at a participating organization and the organization submitted the clinical data needed to calculate a risk score to the registry. Organizations chose whether to have all or only some practitioners participate and could update their practitioner lists during the intervention (after randomization). However, for organizations randomized to the control group, CMS imposed a 20-practitioner cap to limit costs of testing the model. Eligible beneficiaries were aged 40 to 79 years at their initial model visit, without a previous MI or stroke, kidney failure, or hospice use. CMS used registry data and Medicare data to (1) verify beneficiaries’ eligibility, including no Medicare claims indicating MI or stroke since 1999; (2) verify that a visit occurred with a participating practitioner; and (3) calculate a baseline CVD risk score. After a beneficiary was assigned to an organization they could not be assigned to a second organization.

The analytic population for this study was defined as beneficiaries entering the model in 2017 or 2018 (eMethods 2 in Supplement 2 ) with a CVD risk score greater than or equal to 15%. Although performance-based risk-reduction payments were limited to high-risk beneficiaries (CVD risk score ≥30%), CMS anticipated spillover 15 to medium-risk beneficiaries (CVD risk score ≥15% and <30%). As prespecified in the trial protocol, we focused on effects among (1) the combined high- and medium-risk population and (2) only the high-risk group. We analyzed only beneficiaries entering the model in 2017 or 2018 to ensure sufficient time to assess long-term outcomes before the model’s end in 2021.

The outcome measures are defined in eMethods 3 in Supplement 2 , including relevant diagnosis codes. We analyzed the following 2 primary outcomes: (1) first-time CVD events (MIs, strokes, and TIAs), identified using Medicare claims data, and (2) Medicare spending on first-time CVD events (PBPM), before model payments, covering all Parts A and B spending during the hospitalization or emergency department (ED) visit for the event and 90 days after discharge (to cover, for example, rehabilitation services following a stroke). This outcome was the primary spending outcome because we anticipated better power to detect effects than for total Medicare spending.

We further analyzed the following secondary outcomes: (1) first-time CVD events or CVD deaths (due to coronary heart disease or cerebrovascular disease), identified using claims data and underlying cause of death recorded in the National Death Index; (2) Medicare Parts A and B spending PBPM before model payments; (3) Medicare Parts A and B spending PBPM including model payments; (4) all-cause hospitalizations; (5) all-cause outpatient ED visits (including hospital observation stays); (6) office visits; (7) circulatory system–related hospitalizations, limited to stays with 1 of roughly 300 relevant diagnosis codes; (8) circulatory system–related ED visits; and (9) all-cause deaths, identified in the Medicare Enrollment Database. Supplement 2 also contains analyses by cause of death from the National Death Index.

Outcomes were measured from the date the beneficiary entered the model until December 31, 2021, or censoring due to death or losing Medicare fee-for-service coverage, a maximum of 1823 days (5 years).

We estimated effects using Cox proportional hazard models for time-to-event outcomes (first-time CVD events, first-time CVD events or CVD deaths, and all-cause deaths). For Medicare spending on first-time CVD events, we used a 2-part model: a logit model for the probability of a CVD event within 4 years and a linear model for spending conditional on having an event (eMethods 4 in Supplement 2 describes sample restrictions for this analysis, the only with a 4-year follow-up). For all other outcomes, longitudinal linear regression models were used. All analyses used robust standard errors clustered at the organization level. Covariates covered beneficiary-, organization-, and area-level characteristics (eMethods 5 in Supplement 2 ). As with other CMS tests, 16 - 19 we prespecified 7 a P value threshold of .10 (2-tailed test) to balance the risk of type I and type II errors.

We conducted sensitivity tests using more specific outcome definitions for CVD events (eg, excluding TIAs), supplementing time-to-event analyses with logistic models in case the Cox proportional hazards assumption was violated, and trimming the intervention group to mimic the 20-practitioner cap in the control group. We further attributed beneficiaries based on visits with model-participating practitioners and reestimated effects among all beneficiaries who visited the organizations and appeared eligible in Medicare data, regardless of whether an organization provided registry data (eMethods 6 in Supplement 2 ). We also assessed the influence of COVID-19 by calculating outcome trends in 2020 to 2021 by county and checking how much regional differences could bias the study findings

Of 516 organizations randomized, 345 (173 in the model intervention group and 172 in the standard care control group) submitted registry data to enter at least 1 beneficiary into the model ( Figure 1 ). Organizations with model beneficiaries—both intervention and control—were more likely than organizations randomized but without model beneficiaries to be primary care practices and participate in other CMS initiatives (eTable 1 in Supplement 2 ). As expected, the intervention group had more model beneficiaries with high and medium risk (n = 130 578) than the control group (n = 88 286) due to the control group’s practitioner cap. For both groups, the model population comprised slightly more than half the attributed beneficiaries (eFigure 1 in Supplement 2 ), indicating that organizations did not provide data for many beneficiaries aged 40 to 79 years without previous MI or stroke. In both the intervention and control groups, model beneficiaries had similar demographics to attributed beneficiaries not in the model, but had lower mean Medicare spending and hospitalization rates before their model-qualifying visits (eTable 2 in Supplement 2 ).

The participating intervention and control groups that were used to estimate effects were broadly similar. Model-participating organizations ranged in size and included primary care practices, specialty practices, and health centers ( Table 1 ). Model beneficiaries in the intervention and control groups ( Table 2 ) were similar (among high- and medium-risk beneficiaries together) in age (median age, 72-73 years), sex (58%-59% men), race (7%-8% Black and 92%-93% other races, as reported by the participating organizations and verified in the Medicare Enrollment Database, with both based on fixed-category race designations), and baseline CVD risk score (median, 24%). These beneficiaries received substantial care at baseline, with frequent office visits in the year before model entry and most receiving statins or antihypertensives ( Table 2 ). Still, opportunities remained to reduce CVD risk. For example, approximately three-quarters of high-risk beneficiaries had elevated systolic blood pressure (≥130 mm Hg).

Table 3 shows estimated effects on all outcomes. Over a median follow-up of 4.3 years, high- and medium-risk beneficiaries in the intervention group experienced a 3.3% lower rate of CVD events than beneficiaries in the control group ( Table 3 and Figure 2 ; adjusted hazard ratio [HR], 0.97 [90% CI, 0.93-1.00]; P = .09) and a 4.2% lower rate of combined first-time CVD events and CVD deaths (HR, 0.96 [90% CI, 0.93-0.99]; P = .02). These relative effects represent an absolute reduction of 0.3 percentage points in the probability of a CVD event over 5 years (7.8% in the intervention group vs 8.1% in the control group; Figure 2 ) and 0.4 percentage points in the probability of a CVD event or CVD death over 5 years (9.3% vs 9.7%; Figure 2 ). The beneficiaries in the intervention group also experienced a 4.3% lower death rate (HR, 0.96 [90% CI, 0.93-0.98]; P = .01; absolute reduction of 0.5 percentage points over 5 years). Analyses by cause of death showed the largest relative declines (10.6%) among deaths due to coronary heart disease and cerebrovascular disease (eTable 3 in Supplement 2 ). Across outcomes, results were generally more favorable among medium-risk beneficiaries than high-risk beneficiaries, for whom differences between the intervention and control group were not statistically significant for first-time CVD events, combined CVD events and CVD deaths, or all-cause deaths. However, high-risk beneficiaries in the intervention group experienced a relative 14.4% lower death rate than beneficiaries in the control group from coronary heart disease ( P = .03; eTable 3 in Supplement 2 ).

There was no significant difference in Medicare spending on CVD events between the intervention and control groups (effect estimate for high- and medium-risk beneficiaries = −$1.83 PBPM [90% CI, −$3.97 to $0.30]; P = .16). Likewise, overall Parts A and B spending PBPM was similar between the intervention and control groups—both before and including model payments—despite higher rates of all-cause hospitalizations in the intervention group than the control group (3.7% higher among high- and medium-risk beneficiaries). Model payments were small: an estimated $1.24 per high- and medium-risk beneficiary per month. Risk-reduction payments comprised 28% of the total model payments.

Findings were largely robust to sensitivity tests around outcome definition, regression specification, and population composition (eTables 4-7 in Supplement 2 ). For example, high- and medium-risk beneficiaries attributed to the intervention organizations (based on visit history) experienced a 1.1% lower rate of CVD events than those in control organizations. This implies a relative effect of 2.1% for model beneficiaries—similar to the main estimate of 3.3%—assuming that only model beneficiaries experienced effects (eTable 7 in Supplement 2 ). Similarly, analysis results suggest a low risk of bias due to COVID-19. Changes in outcome trends during the pandemic were virtually identical between regions in which the intervention and control group beneficiaries lived (eFigures 2-4 in Supplement 2 ), implying no meaningful influence on effect estimates (eTable 8 in Supplement 2 ).

The Million Hearts Model tested a strategy of providing payments for CVD risk assessment and risk reduction and asking organizations to commit to CVD preventive care. The model reduced the probability of a first-time CVD event over 5 years by 0.3 percentage points and reduced the probability of a first-time CVD event or CVD death by 0.4 percentage points among high- and medium-risk beneficiaries. As with other primary prevention initiatives, small risk differences at an individual level can translate into meaningful effects at the population level. Results of this study suggest 1 averted first-time CVD event for roughly every 400 high- and medium-risk beneficiaries in the model and 1 averted first-time CVD event or CVD death for roughly every 250 high- and medium-risk beneficiaries—numbers that compare favorably to historical values of numbers needed to screen for common cancer screenings. 20 Results did not show effects on the primary outcome of CVD events or the outcome of CVD events or CVD deaths among high-risk beneficiaries alone, although analyses for that population had lower statistical power. There were no statistically significant effects on Medicare spending for any population, despite the intervention group’s increased hospitalization rates.

This study used a prespecified threshold for statistical significance of .10, which is unconventional in the medical literature but common among CMS studies. 16 - 19 A threshold of .10 indicates, by definition, a greater risk of false-positive results than a threshold of .05 with a greater probability of identifying effective interventions. As noted in the American Statistical Association’s 2016 statement on statistical significance, 21 no P value threshold determines whether effects are real. This study’s observed effect on CVD events ( P = .09) is supported by findings for the study’s secondary outcomes and for intermediate model outcomes reported previously. 6

The Million Hearts Model was a pragmatic trial, and organizations’ model engagement varied. Roughly one-third of randomized organizations entered no beneficiaries into the model, generally citing low payments and challenges submitting registry data. Moreover, among organizations with model beneficiaries, most stopped submitting reassessment data needed to qualify for performance-based payments, 22 which could have been recurring payments through 2021, contributing to low model payments overall.

Current guidelines in the US, 9 similar to those in other countries, 23 - 25 recommend that health care practitioners calculate CVD risk scores and use the scores to engage patients in discussions about CVD prevention. Although previous studies of CVD risk scoring interventions have shown improvement in CVD risk factor control, this is the first study of a CVD risk score–focused intervention to demonstrate declines in CVD events. 26

This study did not test risk scoring or other CVD preventive care directly, but tested a policy to incentivize CVD preventive care. Still, results of the study suggest that CVD risk assessment and resulting care were more important than other activities incentivized under the model, which were specific to high-risk beneficiaries. In particular, observed effects were generally larger for medium-risk beneficiaries than high-risk beneficiaries. An earlier Million Hearts Model study showed large increases in CVD risk assessment and subsequent improvements in both CVD medication use and CVD risk factor control. 6 Risk scores might be especially useful for identifying medium-risk beneficiaries, whose risk might otherwise be overlooked. 27 The findings thus bolster the case for current guideline recommendations advocating CVD risk score use, even though we cannot entirely disentangle effects of risk assessment from organizations’ other CVD efforts.

The study results have implications for value-based payment policy. Systematic reviews show that value-based payment initiatives, both overall 28 , 29 and for CVD care, 30 have improved care processes, but few improved long-term outcomes. This randomized pragmatic trial suggests that paying for risk assessment and reduction could improve outcomes of public health importance. However, high rates of model nonparticipation demonstrate the importance of calibrating payments to effort and reducing burden of data sharing.

This study has many strengths. Model participants included various organization types across the US, suggesting broad applicability of findings within the country. The model was large and unusual among CMS models in having a randomized design. 31 Also, the study used administrative data (from Medicare and the National Death Index) to track beneficiaries’ outcomes, regardless of organizations’ model engagement.

This study has several limitations. First, the main limitation is nonparticipation of many randomized organizations and incomplete entry of beneficiary data into the registry, which could have led to systematic differences between the intervention and control groups. Intervention and control beneficiaries remained broadly similar regarding observable characteristics, although regression adjustment had a meaningful influence on effect estimates. Results from the attributed population were generally consistent with results from the model population, strengthening our confidence that estimates represent model effects. Nevertheless, bias due to the selective participation of organizations and beneficiaries cannot be ruled out.

Second, organizations volunteered for random assignment into the model, which limits generalizability of findings. Intervention organizations were likely motivated to implement the model. Other practitioners might not respond equally to model incentives and supports offered from 2017 to 2021. It is unclear how findings would generalize to an older, younger, or sicker population; to secondary prevention of CVD; or to settings outside the US.

Third, CMS payments to the control group to collect clinical data for CVD risk scores could have induced changes in care for the control group. If this happened, it would bias study results toward the null.

Fourth, this study identified first-time CVD events in Medicare claims, which misses events without a hospitalization or ED visit. The risk was particularly acute during the COVID-19 pandemic, when many people likely avoided the hospital, even for CVD events. 32 To address this, effects were estimated on a composite outcome of first-time CVD events (in claims) and CVD deaths. Effect estimates were similar for the 2 outcomes, indicating the model likely reduced first-time MIs and strokes, including fatal cases without claims, by 3% to 4%.

With CMS payments and organizations’ commitment to routine CVD risk assessment, the Million Hearts Model reduced the incidence of first-time MIs and strokes over 5 years, without significant changes in Medicare spending. Results suggest a promising approach to improve CVD outcomes and support guidelines about risk scoring for CVD primary prevention.

Accepted for Publication: September 12, 2023.

Corresponding Author: Laura Blue, PhD, Mathematica, 1100 First St NE, Washington, DC 20002 ( [email protected] ).

Author Contributions: Dr Peterson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Blue, Kranker, Markovitz, Williams, Pu, McCall, Rollison, Markovich, Peterson.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Blue, Kranker, Markovitz, Williams, Magid, Peterson.

Critical review of the manuscript for important intellectual content: Kranker, Markovitz, Powell, Williams, Pu, Magid, McCall, Steiner, Stewart, Rollison, Markovich, Peterson.

Statistical analysis: Blue, Kranker, Markovitz, Powell, Pu, McCall, Steiner, Peterson.

Obtained funding: Kranker, Williams, McCall, Peterson.

Administrative, technical, or material support: Powell, McCall, Steiner, Stewart, Rollison, Markovich, Peterson.

Supervision: Blue, Williams, McCall, Peterson.

Other - drafting of the supplemental materials: Kranker.

Conflict of Interest Disclosures: Dr Powell reported being employed by Mathematica during the conduct of the study. Dr Magid reported receiving grant funding from the National Institutes of Health for projects UH3HL144163-02 and R01HL153154-01 and having a research contract to develop quality care measures for Motive Medical Intelligence. No other disclosures were reported.

Funding/Support: The analyses on which this publication were based were performed under Contract Number HHSM-500-2014-00034I, entitled, “Million Hearts CVD Risk Reduction Model,” sponsored by the Centers for Medicare & Medicaid Services, Department of Health and Human Services.

Role of the Funder/Sponsor: The funder reviewed and approved the study design and the manuscript draft and approved the decision to submit the manuscript for publication.

Disclaimer: The views expressed here are the authors’ and do not necessarily represent views of the funder.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: The authors acknowledge valuable design contributions from Thomas Concannon, PhD (RAND); Leslie Conwell, PhD (Mathematica); and Adam Rose, MD (formerly of RAND); research support from Dan Kinber, MPP, and Rui Wang, PhD (Mathematica); programming support from current and former Mathematica staff Alex Bryce, MS; Elizabeth Holland, BA; Andrew McGuirk, BA; Sandi Nelson, MPP; Lei Rao, MS; and Tyler Rose, MS; editing from John Kennedy, MA (Mathematica); formatting support from Sheena Flowers, BA (Mathematica); and project management from Tessa Huffman, BS (Mathematica). These contributors were paid for their contributions under the study contract. This study builds on interview and survey data collected and analyzed by staff at Mathematica and RAND. This study would not be possible without the CMS model team and other staff at the CMS Innovation Center operating the model and supporting its evaluation; and staff at Deloitte, Econometrica, and Premier who built and maintained the Million Hearts Data Registry.

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