Vicky Mahn-DiNicola, RN, MS, CPHQ

Vicky Mahn-DiNicola is Vice President of Research and Market Insights at Midas+ Xerox, where she serves as a speaker, author and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting and healthcare transformation. A Certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post graduate studies at the University of Arizona, where she continues to serve as Adjunct Faculty.

v mahn dinicolaEDITOR’S NOTE: All individual years listed in this article are fiscal years, unless otherwise noted.

The Centers for Medicare & Medicaid Services (CMS) finalized multiple changes to quality reporting programs for hospitals in the Inpatient Prospective Payment System (IPPS) final rule for 2017, CMS-1655-F, which was posted to the Federal Register on Aug. 22, 2016. This article summarizes the most substantive changes for five hospital quality reporting programs.

Hospital Readmissions Reduction Program
Starting on page 56,973

In 2016 there were 3,464 hospitals in the Hospital Readmissions Reduction Program (HRRP). Of those, 2,665 received penalties for excess readmissions, totaling approximately $420 million across all U.S. hospitals. Twenty-three percent of hospitals had no penalty. The maximum allowable penalty of 3 percent was assigned to only 38 hospitals.

In the previous IPPS rule, the HRRP was expanded to include the isolated coronary artery bypass graft (CABG) population beginning in 2017. The 2016 rule specified the expansion of the pneumonia measure cohort in 2017 to include principal diagnosis codes for aspiration pneumonia, patients with a principal diagnosis of non-severe sepsis with a secondary diagnosis of pneumonia, and patients with only a principal diagnosis of viral or bacterial pneumonia. The expanded pneumonia cohort is expected to increase qualifying cases by as much as 50 percent in many hospitals. 

There were no new clinical cohorts required for future years in the new IPPS final rule, leaving the following six measures in the HRRP for 2017 and beyond:

  • Acute MI 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Heart Failure 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Pneumonia (expanded) 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • COPD 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Total Hip or Knee Arthroplasty 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • CABG 30-Day, All-Cause, Risk-Standardized Readmission Rate   

The applicable discharges for 2018 payment determination will be those occurring from July 1, 2013 to June 30, 2016, so there is nothing you can do to impact penalties for 2018. There are only nine months left to influence penalties for 2019, which are based on discharges occurring from July 1, 2014 through June 30, 2017. The maximum allowable penalty remains capped at 3 percent of a hospital’s Medicare payment.   

Hospital-Acquired Conditions Reduction Program
Starting on page 57,011

The most significant change in the new IPPS final rule for the Hospital Acquired Conditions (HAC) Reduction Program is the adoption of the modified AHRQ PSI-90 composite measure, beginning with payment determination for 2018. Previously, this measure, called “Patient Safety for Selected Indictors,” contained eight individual measures, which will now be expanded to 10. Three measures have been added while PSI 7 Central-line Associated Blood Stream Infections (CLABSI) has been removed due to redundancy with the HAC Reduction Program as well as other programs that include this measure. The final rule also renames the measure as the “Patient Safety and Adverse Events Composite.” 

The modified AHRQ PSI-90 Composite Measure contains the following measures:

  • PSI 3 Pressure Ulcer Rate
  • PSI 6 Latrogenic Pneumothorax
  • PSI 8 Post-op Hip Fracture Rate
  • PSI 9 Post-op Hemorrhage or Hematoma (new)
  • PSI 10 Physiologic/Metabolic Derangement (new)
  • PSI 11 Post-op Respiratory Failure (new)
  • PSI 12 Post-op PE or DVT
  • PSI 13 Post-op Sepsis Rate
  • PSI 14 Wound Dehiscence Rate
  • PSI 15 Accidental Puncture/Laceration

Note: There are substantial revisions to the risk-adjustment methodology for the modified AHRQ PSI-90 Composite, which adds assigned weights for degree of harm to the current volume measure.

Finally, CMS revised applicable periods from previous rules for 2018 and 2019, calling for a 15-month applicable period for 2018 (July 1, 2014 to Sept. 30, 2015), and a 21-month applicable period for 2019 (Oct. 1, 2015 to Sept. 30, 2017). This change was made in order to avoid mixing ICD-9 diagnosis codes with ICD-10 diagnosis codes, the latter of which went into use for all U.S. hospitals on Oct. 1, 2015. Note that the revised risk-adjusted AHRQ software for ICD-10 codes is not expected to be published until late in the 2017 calendar year. 

There are no changes to domain weights, which leaves Domain 1 (containing the modified PSI-90 Composite Measure) at 15 percent of the total score and Domain 2 (containing Hospital-Acquired Infections) at 85 percent for 2018 and beyond. The hospital-acquired infections included in the 2018 HAC Reduction Program and collected for discharges in the 2015 and 2016 calendar years are: 

  • Catheter-Associated Urinary Tract Infections (CAUTI) for ICU and Non-ICU Patients
  • Central-Line Associated Blood Stream Infections (CLABSI) for ICU and Non-ICU Patients
  • Surgical Site Infection for Abdominal Hysterectomy and Colon Surgery
  • Methicillin-Resistant Staphylococcus aureus (MRSA)
  • Clostridium Difficile Infections (CDI)

The most substantive change involved the expansion of CAUTI and CLABSI measures to include both ICU and non-ICU pediatric and adult medical and surgical patients discharged in the 2015 and 2016 calendar years.

The HAC Reduction Program was originally designed so that 25 percent of all hospitals would receive performance penalties; however, current scoring methodologies have resulted in only 21.9 percent of hospitals receiving penalties in 2015 and 23.7 percent in 2016. To correct for this, CMS is revising the scoring methodology, beginning with 2018 payment determination. The revised scoring methodology uses a Winsoried Z-score instead of the previous linear scale. This approach will evaluate performance more favorably for smaller hospitals that have few cases in either Domain 1 or 2. However, it is likely to impact disproportionate share hospitals (DSHs) with a moderately high volume of underprivileged patients. CMS estimates that using this scoring methodology, top-quartile penalties for these hospitals may increase from 28 to 35 percent (affecting about 11 more).  

Hospital Value-Based Purchasing Program
Starting on page 56,979

The Hospital Value-Based Purchasing (VBP) Program, which applies to all subsection (d) hospitals in the U.S., has a current funding pool capped at 2 percent of all hospital base operating DRG payments, leaving approximately $1.8 billion ($1.489 billion in the 2016 fiscal year) available for funding value-based payment incentives in 2017.

Similar to the changes in the HAC Reduction Program described above, in order to keep ICD-9 and ICD-10 claims separate, this year’s rule modifies the timelines for the applicable period in 2018 for the PSI-90 Composite Measure to the 15 months between July 1, 2014 and Sept. 30, 2015 (this measure is currently in the Patient Safety domain). However, due to the required timelines associated with the VBP rulemaking process, which requires that measures be displayed in Hospital Compare for a full year prior to inclusion in the VBP Program, CMS is unable to adopt the modified PSI-90 Composite measure for 2019. Future rulemaking is expected to incorporate the modified version, as well as revised timelines for 2019 to align with other programs. 

Other changes include the renaming of the “Patient/Caregiver Experience of Care/Care Coordination Domain,” which reflects results from the Hospital Consumer Assessment of Health Plans and Systems (HCAHPS) survey. Effective 2019, this will be known as the “Person and Community Engagement” domain, and will retain its weighting of 25 percent. There were no changes to the existing HCAHPS measures in this domain. Also in 2019, the CAUTI and CLABSI measures will be expanded to include both ICU and non-ICU patients.

The big surprise came with the announcement that CMS is proposing to remove the PSI-90 Composite measure from the Value-Based Purchasing program beginning with 2019 payment determination, although they are suggesting that the adoption of the modified version of the PSI-90 Composite measure may be adopted in future years. 

The 2021 program will include the expanded pneumonia measure cohort in the 30-day mortality measure, as described above in the Hospital Readmission Reduction Program. In addition, two episode-of care payment measures will be added to the Care Efficiency domain for 2021, which currently contains only a single measure: Medicare Spending per Beneficiary (MSPB). These include:

  • Hospital-level, Risk-standardized Payment Associated with 30-day Episode-of-Care for Acute MI (NQF No. 2431)
  • Hospital-level, Risk-standardized Payment Associated with 30-day Episode-of-Care for Heart Failure (NQF No. 2436)

These new measures will use a baseline period of July 1, 2012 to June 30, 2015 to compare performance for discharges occurring from July 1, 2017 to June 30, 2019, so hospitals still have some time to begin examining costs for these populations prior to the applicable period. 

It is interesting to note that the National Quality Forum (NQF) Measures Application Partnership (MAP) vote on the approval of these measures reflected concerns with both of these proposed measures, including the lack of risk adjustment using sociodemographic variables and the potential that they overlap with the existing Medicare Spending per Beneficiary measure. Fifty-eight percent of NQF MAP committee members voted not to support the Acute MI 30-day Episode-of-Care measure, and 65 percent voted not to support the Heart Failure 30-day Episode-of-Care measure. 

Hospital Inpatient Quality Reporting Program
Starting on page 57,111

Perhaps the biggest surprise in this year’s final rule was an unexpected reduction in the number of electronic clinical quality measures (eCQMs) required for submission for discharges occurring in the 2017 calendar year (for 2019 payment determination).  After a great deal of public comment about the barriers and challenges facing hospitals in achieving accurate eCQM data, CMS modified the original proposal to transition from a requirement of four to 15 eCQMs to only eight. Hospitals may select which eight measures they submit. However, in order to meet Hospital IQR program requirements, a full year of data for discharges taking place from Jan. 1, 2017 to Dec. 31, 2017 will be required for electronic submission to CMS. 

Hospitals may submit their data quarterly, biannually, or annually using a QRDA-1 format from a 2014 or 2015 version of certified healthcare reporting technology (CEHRT) software.  Submissions for discharges must be complete by Feb. 28, 2018.

Thirteen eCQM measures have been finalized for removal, leaving the following 15 for hospitals to choose from when selecting their eight eCQM measures: 

  • AMI-8a PCI Within 90 Minutes of Arrival
  • CAC-3 Home Management Plan Given to Patient or Caregiver
  • EHDI-1a Hearing Screening Prior to DC
  • ED-1 Mean Time from Arrival to ED Departure for Admitted ED Patients
  • ED-2 Admit Decision Time to ED Departure for Admitted Patients
  • PC-01 Elective Delivery
  • PC-5 Exclusive Breast Milk Feeding
  • STK-2 Discharged on Antithrombotic
  • STK-3 Anticoagulation for Atrial Fib/Flutter
  • STK-05 Antithrombotic Therapy by End of Hospital Day 2
  • STK-06 Discharged on Statin Meds
  • STK-8 Stroke Education
  • STK-10 Assessed for Rehabilitation
  • VTE-1 VTE Prophylaxis
  • VTE-2 ICU VT Prophylaxis

CMS is encouraging hospitals to submit early and to use pre-submission testing tools, such as the CMS Pre-Submission Validation Application (PSVA), which can be downloaded from Quality Net at https://cportal.qualitynet.org/QNet/pgm_select.jsp. Note that these tools check for file formatting errors, not data accuracy.  

Not surprisingly, the number of chart-abstracted measures continues to decline. In this year’s final rule, both the eCQM and chart-abstraction versions of STK-4 Thrombolytic Therapy for Acute Ischemic Stroke and VTE-5 Discharge Instructions have been removed. These measures have “topped out” statistically and are no longer useful for public reporting. In addition, the electronic version of VTE-5 has met substantial technical feasibility issues in capturing specific clinical details needed for accurate measure computation. The following six chart-abstracted measures remain for 2017 and beyond:

  • ED-1 Median Time from ED Arrival to ED Departure for Admitted Patients **
  • ED-2 Admit Decision Time to ED Departure Time for Admitted Patients **
  • PC-01 Elective Delivery Prior to 39 Completed Weeks of Gestation **
  • VTE-6 Incidence of Potentially Preventable VTE
  • IMM-2 Influenza Immunization
  • Severe Sepsis and Septic Shock: Management Bundle (Composite Measure)

** Note: ED-1, ED-2, and PC-01 are required for chart-abstracted submission even if hospitals select these as three of their eight required eCQM measures. 

This year’s final rule marks the end of the remaining Pneumonia and Surgical Care Improvement Project (SCIP) core measures for submission to CMS due to the fact that they have also statistically “topped out.” In addition, the Healthy Term Newborn Measure has been removed due to the fact that the measurement steward has changed the measure construct to focus on unexpected complications in newborns. These changes are effective with discharges taking place in the 2017 calendar year for 2019 payment determination.

No changes were made to the CMS claims-based measures evaluating mortality and 30-day unplanned readmissions or to the National Healthcare Safety Network (NHSN) Infection measures. However, the PSI-90 Composite measure in the complications domain will transition to the modified PSI-90 Patient Safety and Adverse Events Composite beginning with the 2018 program, following the same specifications and timelines as described above in the HAC Program.   

Two “structure of care” Measures were also retired for 2019 payment determination.  These include the web-based reporting measures for participation in a systematic clinical database registry for nursing sensitive care and general surgery. These changes reflect CMS’s opinion that reporting to these registries bears little correlation to favorable patient outcomes. The remaining structure of care measures for the patient surgery checklist and the patient safety culture reporting requirements remain in effect.

Additional proposed changes to the Hospital IQR Program were finalized, including minor revisions to data validation procedures, which will require 200 randomly selected hospitals that are submitting eCQMs to provide copies of their electronic health records for 32 cases. These records will be used to validate data accuracy of electronic measures, compared to the details of the patient encounter found in the clinical documentation. Requirements for full payment in 2019 will not require a given percentage of data accuracy, but instead will be based on whether the hospitals comply with submitting at least 24 of the 32 (75 percent) requested records in a timely manner (30 days from the request) to CMS.   

An additional 400 hospitals not selected for eCQM validation will be randomly selected to submit patient records for chart-abstracted measures, in addition to another 200 hospitals targeted for validation because of abnormal or conflicting data patterns or late data submissions. As required in past years, chart-abstracted validations will require a minimum agreement rate of 75 percent  in order for hospitals to receive full payment updates in 2019.

Three new clinical episode-based payment measures were finalized for the 2019 payment determination, which CMS will calculate from Medicare claims data. These include:

  • Aortic Aneurysm Procedure Clinical Episode-Based Payment
  • Cholecystectomy/Common Duct Exploration Clinical Episode-Based Payment
  • Spinal Fusion Clinical Episode-Based Payment

Finally, the expanded pneumonia cohort was designated as the third “excess days” measure for 2019 payment determination, in addition to those for acute MI and heart failure. For these measures, emergency department (ED) readmissions are counted as a half day, observation status readmissions are rounded to the nearest half-day, and inpatient readmissions are counted as full days.

CMS is inviting public comment for future measures being considered as well. These include:

  • Risk adjustment of the MORT-30-STK Hospital 30-Day, All-Cause, Risk-Standardized Mortality Rate Following Acute Ischemic Stroke using the National Institute of Health (NIH) Stroke Scale as an Assessment of Stroke Severity
  • NHSN Antimicrobial Use Measures to evaluate antibiotic use compared to predicted antibiotic use in both adult and pediatric populations
  • Behavioral health measures for patients in acute-care hospital beds
  • Stratification of hospital IQR measures by race, ethnicity, gender, and disability

Hospital-based Inpatient Psychiatric Services Quality Reporting Program
Starting on page 57,236

There are currently 13 measures required for submission to CMS for hospital-based inpatient psychiatric facilities providing services during the 2016 calendar year that impact payment determination in 2018. These include:

  • Hours of Physical Restraint Use
  • Hours of Seclusion Use
  • Patients Discharged on Multiple Antipsychotic Medications with Appropriate Justification
  • Follow-up after Hospitalization for Mental Illness
  • Alcohol Use Screening
  • Alcohol Brief Intervention Provided or Offered (and subset measure for intervention)
  • Tobacco Use Screening
  • Tobacco Use Brief Intervention Provided or Offered (and subset measure for intervention)
  • Influenza Immunization
  • Influenza Vaccination Coverage among Healthcare Personnel
  • Assessment of Patient Experience of Care
  • Use of an Electronic Health Record

Previous rulemaking from last year’s final rule requires two new measures to begin with services delivered in the 2017 calendar year (2019 payment determination) for patients discharged from an inpatient facility to home (self-care) or any other site of care. These include:

  • Transition record with specified elements received by discharged patients
  • Timely transmission of transition record

The following new measures, one chart-abstracted and one claims-based, were finalized in this year’s rule for the Hospital-based Inpatient Psychiatric Services (HBIPS) Quality Reporting program for the 2017 calendar year (2019 payment determination):

  • SUB-3 Alcohol and Other Drug Use Disorder Treatment Provided or Offered at Discharge and SUB-3a Alcohol & Other Drug Use Disorder Treatment at Discharge (chart-abstracted)
  • 30-Day All-Cause Unplanned Readmission Following Psychiatric Hospitalization in an IPF (claims-based)

Finally, a modification to the Screening for Metabolic Disorder measure, originally proposed to begin with July 1, 2016 discharges, was pushed forward to begin with Jan. 1, 2017 discharges for 2019 payment determination. This measure evaluates the screening of psychiatric patients for body mass index, blood pressure, lipids, and either a glucose or HgA1c level. This modification requires the denominator cohort to exclude psychiatric patients with a length of stay greater than 365 days or less than or equal to three days. In previous measure specifications, the exclusion criteria were specified as less than three days. The data devil is in the details. 

About the Author

Vicky Mahn-DiNicola is the VP of clinical analytics and research for MidasPlus, Inc. a Xerox company for which she serves as a speaker, author, and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting, and healthcare transformation. A certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post-graduate studies at the University of Arizona, where she continues to serve as adjunct faculty. 

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The Centers for Medicare & Medicaid Services (CMS) proposed multiple changes to quality reporting programs for hospitals in the 2017 Inpatient Prospective Payment System (IPPS) Proposed Rule, CMS-1655-P, which was posted to the Federal Register on April 27, 2016. Public comments are due by June 17, 2016, with the final rule expected to be published in August 2016. This article summarizes the most substantive proposed changes for five hospital quality reporting programs.

 

Hospital Readmission Reduction Program
Starting on page 25,094

In 2016 there were 3,464 hospitals in the Hospital Readmission Reduction Program (HRRP).  Of those, 2,665 received penalties for excess readmissions, totaling approximately $420 million. Twenty-three percent of participating hospitals had no penalty. The maximum allowable penalty of 3 percent was assigned to only 38 hospitals.

In the previous 2016 IPPS rule, the HRRP was expanded to include the isolated coronary artery bypass graft (CABG) population beginning in 2017. The 2016 rule specified the expansion of the pneumonia measure cohort in 2017 to include principal diagnosis codes for aspiration pneumonia and patients with a principal diagnosis of non-severe sepsis with a secondary diagnosis of pneumonia, in addition to those with only a principal diagnosis of viral or bacterial pneumonia. The expanded pneumonia cohort is expected to increase qualifying cases by as much as 50 percent in many hospitals. 

There were no new clinical cohorts proposed for future years in the IPPS 2017 Proposed Rule, leaving the following six measures in the HRRP for 2017 and beyond:

  • Acute MI 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Heart Failure 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Pneumonia (expanded) 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • COPD 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • Total Hip or Knee Arthroplasty 30-Day, All-Cause, Risk-Standardized Readmission Rate
  • CABG 30-Day, All-Cause, Risk-Standardized Readmission Rate   

The applicable discharges for 2017 will be those from July 1, 2012 to June 30, 2015, and the maximum allowable penalty will remain capped at 3 percent of a hospital’s Medicare payment.    

Hospital-Acquired Conditions Reduction Program
Starting on page 25,117

The most significant change proposed in the IPPS rule for the Hospital Acquired Conditions (HAC) Reduction Program is the adoption of the modified AHRQ PSI-90 Composite Measure, beginning with payment determination for 2018. Previously, this measure, called “Patient Safety for Selected Indictors,” contained eight individual measures, and this will now be expanded to 10. Three measures have been added, while PSI 7 Central-line Associated Blood Stream Infections (CLABSI) has been removed due to redundancy with the HAC Reduction Program, as well as across other programs where this measure is being reported.  The proposed rule also calls for a modification in the measure name to “Patient Safety and Adverse Events Composite.” 

The modified AHRQ PSI-90 Composite Measure contains the following measures:

  • PSI 3 Pressure Ulcer Rate
  • PSI 6 Latrogenic Pneumothorax
  • PSI 8 Post-op Hip Fracture Rate
  • PSI 9 Post-op Hemorrhage or Hematoma (new)
  • PSI 10 Physiologic/Metabolic Derangement (new)
  • PSI 11 Post-op Respiratory Failure (new)
  • PSI 12 Post-op PE or DVT
  • PSI 13 Post-op Sepsis Rate
  • PSI 14 Wound Dehiscence Rate
  • PSI 15 Accidental Puncture/Laceration

Note: The risk-adjustment methodology for the modified AHRQ PSI-90 Composite measure includes substantial revisions, which include weightings for degree of harm in addition to volume.


 Finally, CMS proposed revised applicable periods from previous rules for the 2018 and 2019 fiscal years, calling for a 15-month applicable period for 2018 (July 1, 2014 to Sept. 30, 2015) and a 21-month applicable period for 2019 (Oct. 1, 2015 to Sept. 30, 2017). This proposed change was made in order to avoid mixing ICD-9 coding diagnosis codes with ICD-10 codes, which were implemented for all U.S. hospitals on Oct. 1, 2015. Note that the revised risk-adjusted AHRQ software using ICD-10 codes is not expected to be published until late 2017. 

No changes to the domain weights were proposed from previous rulemaking, leaving Domain 1 (containing the modified PSI-90 Composite measure) at 15 percent of the total score and Domain 2 (containing hospital-acquired infections) at 85 percent for 2018 and beyond. The hospital-acquired infections included in the 2017 HAC Reduction Program, and collected for discharges in CY 2014 and 2015, are:

  • Catheter-Associated Urinary Tract Infection (CAUTI) for ICU Patients Only
  • Central-Line-Associated Blood Stream Infections (CLABSI) for ICU Patients Only
  • Surgical Site Infection for Abdominal Hysterectomy and Colon Surgery
  • Methicillin-Resistant Staphylococcus Aureus (MRSA)
  • Clostridium Difficile Infections (CDIs)

The same infections are included for 2018, with the expansion of CAUTI and CLABSI measures to include both ICU and non-ICU pediatric and adult medical and surgical patients discharged in the 2015 and 2016 calendar years.

The HAC Reduction Program was originally designed so that 25 percent of all participating hospitals would receive performance penalties; however, current scoring methodologies have resulted in only 21.9 percent of hospitals receiving penalties in 2015 and 23.7 percent in 2016. To correct for this, CMS is proposing a revised scoring methodology beginning with 2018 payment determination. The revised scoring methodology uses a Winsoried z-score instead of a linear scale, which was previously used to determine domain points. This approach will more favorably evaluate performance for smaller hospitals with few cases in either Domain 1 or 2. However, it is likely to impact disproportionate share hospitals (DSHs) with a moderately high volume of underprivileged patients. CMS estimates that top-quartile penalties for these hospitals may increase from 28 to 35 percent (representing approximately 11 more hospitals) using this proposed scoring methodology.  

Hospital Value-Based Purchasing Program
Starting on page 25,099

The Hospital Value-Based Purchasing (VBP) Program for 2017, which applies to all subsection (d) hospitals in the U.S., has a current funding pool capped with estimated funds at $1.7 billion ($1.489 billion in 2016).

Similar to the proposed changes in the HAC Reduction Program described above, in order to keep ICD-9 and ICD-10 claims separate, this year’s Proposed Rule modifies the timelines for the applicable period in 2018 for the PSI-90 Composite Measure to the 15 months between July 1, 2014 and Sept. 30, 2015. (This measure is currently in the Patient Safety Domain.) However, due to the required timelines associated with the VBP rulemaking process, which requires that measures be displayed in Hospital Compare for a full year prior to inclusion in the VBP Program, CMS is not yet permitted to propose adoption of the modified PSI-90 Composite measure for 2019. Future rulemaking is expected to incorporate the modified version as well as revised timelines for 2019 to align with other programs. 

Other proposed changes include the renaming of the “Patient/Caregiver Experience of Care/Care Coordination Domain,” which reflects results from the Hospital Consumer Assessment of Health Plans and Systems (HCAHPS) survey. Effective 2019, this domain will be renamed as the “Person and Community Engagement” Domain and will retain its weighting of 25 percent. There were no proposed changes to the existing HCAHPS measures in this domain. Also in 2019, the CAUTI and CLABSI measures will be expanded to include both ICU and non-ICU patients. 

Proposed changes for the 2021 program include the addition of two Episode-of-Care Payment Measures to the Care Efficiency Domain, which currently contains only a single measure for Medicare Spending per Beneficiary (MSPB). These proposed new measures include:

  • Hospital-level, Risk-standardized Payment Associated with 30-day Episode-of-Care for Acute MI (NQF No. 2431)
  • Hospital-level, Risk-standardized Payment Associated with 30-day Episode-of-Care for Heart Failure (NQF No. 2436)

It is interesting to note that the National Quality Forum (NQF) Measures Application Partnership (MAP) vote on the approval of these measures reflected concerns with both of them, including the lack of risk adjustment using sociodemographic variables and the potential that they overlap with the existing Medicare Spending per Beneficiary measure. Fifty-eight percent of NQF MAP committee members voted not to support the Acute MI 30-day Episode-of-Care Measure, and 65 percent voted not to support the Heart Failure 30-day Episode-of-Care Measure. 

Finally, minor modifications to the scoring methodology for the cost efficiency measures were proposed. If finalized, the formulas for the calculation of achievement and improvement points for this domain will be modified. 

Hospital Inpatient Quality Reporting Program
Starting on page 25,173

In order to align with the electronic health record (EHR) Incentive program, CMS is proposing to transition from four to 15 mandatory reported electronic clinical quality measures (eCQMs) for 2017 calendar-year discharges (for 2019 fiscal-year payment determination). If the proposed rule is finalized, a full year of data for 2017 will be required for electronic submission to CMS in QRDA-1 format using either a 2014 or 2015 version of certified healthcare reporting technology (CEHRT) software.

A total of 13 eCQMs have been proposed for removal, leaving the following 15 eCQM measures required for electronic reporting beginning in 2017 for 2019 payment determination:

  • AMI-8a PCI Within 90 Minutes of Arrival
  • CAC-3 Home Management Plan Given to Patient or Caregiver
  • EHDI-1a Hearing Screening Prior to DC
  • ED-1 Mean Time from Arrival to ED Departure for Admitted ED Patients
  • ED-2 Admit Decision Time to ED Departure for Admitted Patients
  • PC-01 Elective Delivery
  • PC-5 Exclusive Breast Milk Feeding
  • STK-2 Discharged on Antithrombotic
  • STK-3 Anticoagulation for Atrial Fib/Flutter
  • STK-05 Antithrombotic Therapy by End of Hospital Day 2
  • STK-06 Discharged on Statin Meds
  • STK-8 Stroke Education
  • STK-10 Assessed for Rehabilitation
  • VTE-1 VTE Prophylaxis
  • VTE-2 ICU VT Prophylaxis

Two chart-abstracted measures have been proposed for removal, including STK-4 Thrombolytic Therapy for Acute Ischemic Stroke and VTE-5 VTE Discharge Instructions, leaving only the following six chart abstracted measures:

  • ED-1 Median Time from ED Arrival to ED Departure for Admitted Patients **
  • ED-2 Admit Decision Time to ED Departure Time for Admitted Patients **
  • PC-01 Elective Delivery Prior to 39 Completed Weeks of Gestation **
  • VTE-6 Incidence of Potentially Preventable VTE
  • IMM-2 Influenza Immunization
  • Severe Sepsis and Septic Shock: Management Bundle (Composite Measure)

** Note:  ED-1, ED-2 and PC-01 are also required as part of the 15 electronic eCQM measures for 2017 discharges (2019 payment determination). If the proposed rule is finalized, there will no longer be an option to report either chart-abstracted or electronically for these three measures.Submission of both electronic and chart-abstracted measures will be required.

This Proposed Rule marks the end of the remaining Pneumonia and Surgical Care Improvement Project (SCIP) core measures for submission to CMS, due to the fact that they have statistically “topped out.” In addition, the Healthy Term Newborn Measure is proposed for removal due to the fact that the measurement steward has changed the measure construct to focus on unexpected complications in newborns. These changes, if finalized, will be effective with 2017 discharges for 2019 payment determination.


 

No changes were proposed in the CMS claims-based measures evaluating mortality and 30-day unplanned readmissions or to the National Healthcare Safety Network (NHSN) Infection Measures. However, the PSI-90 Composite measure in the complications domain is proposed to transition to the modified PSI-90 Patient Safety and Adverse Events Composite beginning in the 2019 program, following the same specifications as described above in the HAC Program proposed rule for 2019. 

Two “structure of care” measures are proposed for retirement effective for 2019 payment determination. These include the Web-based reporting measures for participation in a systematic clinical database registry for nursing sensitive care and general surgery. These proposed changes reflect CMS’s opinion that reporting to these registries has little correlation to favorable patient outcomes. The remaining structure of care measures for the patient surgery checklist and the patient safety culture reporting requirements remain in effect.

Additional proposed changes to the Hospital IQR Program include minor revisions to data validation procedures, which will require 200 randomly selected hospitals that are submitting eCQMs to provide copies of electronic health records for 32 cases. Requirements for full payment in 2019 include the submission of 24 of the 32 (75 percent) requested records from eCQM measures to CMS for validation, although scoring of data collection or compilation accuracy is not considered in the proposed validation process for eCQMs. 

An additional 400 hospitals not selected for eCQM validation will be randomly selected to submit patient records for chart-abstracted measures, as well as another 200 hospitals targeted for validation because of abnormal or conflicting data patterns or late data submissions. As in past years, chart-abstracted validations will require a minimum of 75-percent agreement rates in order for hospitals to receive full payment updates in 2019.

Three new clinical episode-based payment measures are proposed for 2019 payment determination, which CMS will calculate from Medicare claims data. These include:

  • Aortic Aneurysm Procedure Clinical Episode-Based Payment
  • Cholecystectomy/Common Duct Exploration Clinical Episode-Based Payment
  • Spinal Fusion Payment- Spinal Fusion Clinical Episode-Based Payment

Finally, the expanded pneumonia cohort is proposed as the third “excess days” measures for 2019 payment determination, adding to the excess days measures for acute MI and heart failure. These measures count the days associated with emergency department (ED) readmissions (counted as a half day), observation status readmissions (rounded to the nearest half day), and inpatient readmissions (counted as full days).

Future measures being considered are invited for public comment. These include:

  • Risk adjustment of the MORT-30-STK Hospital 30-Day, All-Cause, Risk-Standardized Mortality Rate Following Acute Ischemic Stroke using the National Institute of Health (NIH) Stroke Scale as an assessment of stroke severity
  • NHSN Antimicrobial Use Measures to Evaluate Antibiotic Use Compared to Predicted Antibiotic Use in Both Adult and Pediatric Populations
  • Behavioral Health Measures for Patients in Acute-Care Hospital Beds
  • Stratification of Hospital IQR Measures by Race, Ethnicity, Gender, and Disability

Hospital-based Inpatient Psychiatric Services Quality Reporting Program
Starting on page 25,238

The following new measures, one chart-abstracted and one claims-based, are proposed for the Hospital-based Inpatient Psychiatric Services (HBIPS) Quality Reporting program for 2017 (2019 Payment Determination):

  • SUB-3 Alcohol and Other Drug Use Disorder Treatment Provided or Offered at Discharge and SUB-3a Alcohol and Other Drug Use Disorder Treatment at Discharge (chart abstracted)
  • 30-Day All-Cause Unplanned Readmission Following Psychiatric Hospitalization in an IPF (claims-based)

Finally, a modification to the Screening for Metabolic Disorder Measure, which begins with discharges July 1, 2016 and evaluates the screening of psychiatric patients for body mass index, blood pressure, lipids, and either a glucose or HgA1c, would require the denominator cohort to exclude psychiatric patients with a length of stay ofless than or equal to three days. Previously, the exclusion criterion was less than three days.

Comments to CMS on these proposed changes may be electronically submitted at http://www.regulations.gov by 5 p.m. EST on June 17, 2016.

Vicky Mahn-DiNicola is the VP of clinical analytics and research for MidasPlus, Inc. a Xerox Company for which she serves as a speaker, author, and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting, and healthcare transformation. A Certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post-graduate studies at the University of Arizona, where she continues to serve as adjunct faculty. 

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This past Christmas, my 28-year-old son walked himself into the emergency department complaining of blurry vision, severe thirst, and headache. His blood glucose was off the scale at 1350. After a costly 13-day hospital stay, which included two days in the ICU, we came home with a discharge diagnosis of Type I diabetes. Having no family history of this on either side, I started wondering what triggered this auto-immune condition for my now insulin-dependent son. 

Just like Shirley MacLaine, who played the controlling and over-protective mother in the movie Terms of Endearment (and told her adult daughter with a lump in her armpit that it was “probably just a swollen gland…you never learned how to properly use a washcloth while bathing”) I resorted to a “mom-ism” inspired by years of subtle observations as both a nurse and a mother. This has to be connected to my son’s poor state of dental and oral hygiene, I thought, as he never did learn how to brush and floss right. Little did I know then that my maternal intuition was actually based in medical research, which shows a link between periodontal disease and diabetes. While periodontal disease is not known to be causative in nature, routine dental exams may lead to earlier diagnosis and treatment for some diabetic patients, which ultimately could reduce healthcare costs.

As I reviewed the literature, I was surprised to discover that dentists are often the first to recognize leukemia and other hematologic disorders such as acquired neutropenia, from the presence of gingivitis. In fact, at least 16 systemic diseases have been linked to periodontitis, including coronary heart disease, cerebrovascular disease, and erectile dysfunction. These diseases are thought to be associated with periodontal disease because they generally contribute to either a decreased host resistance to infections or to dysfunction in the connective tissue of the gums, increasing patient susceptibility to inflammation-induced destruction.

The literature was abundantly clear that more research in this field is needed, and I became excited about the “big data” analytics opportunities in this seemingly untapped field of research. If researchers and data scientists could merge large medical claims and outcomes data with dental claims data, it theoretically would be possible to discover correlations between periodontal disease states and other illnesses and medical disorders. We also could begin to incorporate other types of data into the analysis, including socioeconomic determinants and environmental factors. But that realization caused me to wonder why others haven’t embarked on this endeavor. 

Dentistry is one of the major healthcare professions and as such, it includes a number of subspecialties. It has its own educational institutions, governing bodies (the American Dental Association, for example), and specialty boards, such as those for periodontics and orthodontics. However, despite the fact that periodontal disease has been recognized and treated for at least 5,000 years, dentistry historically has been practiced in isolation from medicine. 

Rarely do medical researchers include aspects of periodontal disease in their basic and clinical research. This is not because there isn’t a recognition that there are potentially important interactions between orofacial systems and other systems in the body. Rather, it is related in part to the fact that diagnostic data from dentistry simply does not exist.

Dentists in private practice and in healthcare institutions use billing systems based on the Current Dental Terminology (CDT) taxonomy, which is specifically designed for dental procedures; however, codes for diseases and diagnostics are not currently used in the dental industry. In 2003, the U.S. Department of Health and Human Services (HHS) purchased rights to SNOMED-CT (Systematized Nomenclature of Medicine – Clinical Terms) from the College of American Pathologists. SNOMED-CT includes more than 6,000 embedded terms to describe dental diagnostics, within a taxonomy known as SNODENT (Systematized Nomenclature of Dentistry). However, much work remains in order to establish an accurate, efficient, and reliable ontology for dentistry. For example 618 SNODENT terms (9.52 percent) are either retired, duplicates, or ambiguous. Another 1,203 of SNODENT terms (18.53 percent) have slightly different descriptions than SNOMED descriptions, and 437 (6.73 percent) have different meanings entirely. 

While there has been some efforts to establish an ontology for dental diagnoses, few incentives exist today that would encourage rapid and broad adoption by dentistry professionals. Following the medical model, such incentives can come in the form of mandated quality reporting to assist the consumer in selection of quality dental providers. The National Quality Forum (NQF) offers a handful of endorsed measures stewarded by the American Dental Association on behalf of the Dental Quality Alliance. However, most of these metrics are geared toward children and prevention of dental issues, such as fluoride treatments and frequency of exams, and are not designed for outcomes research.

Another impediment to the adoption of diagnostic coding methodologies for dental practice is that it will cost money. Unlike the meaningful use electronic medical record (EMR) incentive programs, which have provided financial incentives to medical providers to implement EMR technologies that adhere to a defined set of standards, dentistry lacks both national standards and financial incentives. Whether diagnostic codes are captured through provider documentation and chart review by designated medical record coders, or captured using computerized systems at the point of care, financial incentives will be a critical component in the adoption of dentistry coding practices.

Emerging technologies such as voice recognition software and natural language processing (NLP) can be used to turn spoken or written language into structured codes able to be consumed by an analytics engine to create predictive analytics. However, for these technologies to be successful, they must be incorporated into the provider’s workflow as well as affordable.


 The final challenge addressed here is the issue of linking dental claims for individual patients to their medical and hospital claims. In order for researchers and analytics vendors to perform longitudinal data analysis, encounters or visit records must be patient-centric. Since Medicare doesn’t manage dental claims for Medicare beneficiaries, these processes must be established independently of the government and easy for all payors to support. Although some analytics vendors have been more successful than others in linking claims across multiple points of service and over time, this challenge must be addressed by a national standards body with enough clout to change HIPAA regulations so that research can be conducted on longitudinal blinded and linked clinical data. 

To date, no single entity has proposed a national clearinghouse of combined all-payor dental and medical claims data for the purposes of research and outcomes evaluation. However, partnerships between local dental practices and accountable care organizations (ACOs) or large employers could incentivize data sharing so that a sufficient amount of longitudinal data eventually could be obtained to begin this work. Additionally, a national focus on creating a standardized dental ontology and incentivizing data sharing could be created through partnerships with the Centers for Medicare & Medicaid Services (CMS), the American Medical Association, the American Dental Association, the Dental Quality Alliance, and other organizations such as AHRQ, NQF, the College of American Pathologists, and AHIMA (American Health Information Management Association). 

These are just a few of the essential building blocks to creating next-generation analytics and clinical research that brings a “whole person” view into greater focus and supports a true population health management strategy. It is apparent that the answer lies in a choreographed solution involving an integrated definition of medicine that includes dentistry, as well as healthcare finance, technology, and politics.  

Ms. Mahn-DiNicola will be addressing the American Dental Association on this topic at its convention in Chicago on April 19, 2016. 

About the Author

Vicky Mahn-DiNicola is vice president of research and market insights at Midas+ Xerox, where she serves as a speaker, author, and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting, and healthcare transformation. A certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post-graduate studies at the University of Arizona, where she continues to serve as adjunct faculty. 

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I have a horse that recently developed an abscess in her ear, which required veterinary treatment. So I called my vet: a good old-fashioned country doctor who came out to the house in his big blue pickup truck. Like many older veterinarians, he kept his medical records in a little notebook, which he faithfully updated with each horse’s treatment record after every visit. 

Sadly, I found, my vet had retired. So I called the equine veterinary clinic that took over my old vet’s practice. The new vet and her assistant rolled up in a big, shiny white truck and proceeded to pop open its sides, revealing a fully stocked portable equine hospital, complete with an onboard computer system. To my complete surprise, all the medical records for my horses were already in the system. They had all the vaccination records, results of tests, and even a consultant’s report from (believe it or not) an equine ophthalmologist that came out last year for one of my other horses that had developed a case of acute glaucoma. It’s always been fascinating to me how similar diseases and medical treatments are for animals and humans, and the language we use to describe them.

I watched with complete amazement as the vet called out results of her assessment to her assistant, who served as a scribe and typed everything into the electronic medical record (EMR), including the horse’s vital signs. Now, this equine EMR system had an incredible user interface, and they even uploaded pictures of my horse and her wound. When they were done sedating her and performing an incision and drainage, they emailed me a copy of the bill, along with discharge instructions that contained a list of her medications, instructions on how to change the dressing, and when to call for a follow-up visit. I learned that the portable EMR system syncs up to the office at the end of each day, where records can be shared with other vets at the clinic who later might have to cover for the primary care vet. Vets also can review a single episode of care for a patient via a mobile app, which promotes continuity of care and coordination between specialists when necessary. The system even allows for visualization of radiology images and can download lab results. Last but not least, it tracks their inventory and supply management, prompts for billing, and tracks accounts receivable. 

As an experienced vendor in healthcare IT systems and a studied follower of the meaningful use initiatives, my jaw dropped. I mean, this thing puts most EMR systems I’ve seen in human hospitals and doctor’s offices to shame. The only thing I’ve seen that even comes close is the EMR system used by Walgreens at their TakeCare™ Clinics. Now, I realize that horses don’t face the same complexities with protected health information and HIPAA requirements that people do, but what the hay!? If a local veterinary practice can successfully transition to a fully integrated electronic medical record (it took my vet organization less than a month to implement and train its staff), why is it so difficult for “people doctors” and healthcare systems? What I learned is astonishing.   

First, the cost for veterinarians to implement an EMR is pennies to the dollar as compared to human EMR systems − even though it’s the very much the same technology. The most sophisticated system I found cost less than $25,000 to implement, with some systems as low as $300 per month on a subscription pricing model. There are no regulatory certifications to jump through – and no one had to incentivize the veterinary industry to move into the electronic world. It was simply a matter of allowing vets to practice more efficiently, grow their practices, and meet customer expectations (meaning the horse and cattle owners in the community) for better care and service. This is simply free enterprise at its best. And it’s not just equine veterinarians who are broadly adopting EMR technologies. These same innovations are being implemented in small animal hospitals all across the country. This is no surprise to me, given that Americans spent $47.7 billion on their pets last year – and that doesn’t include the livestock industry!    

I am now certain that we could learn a lot from veterinary medicine in terms of creating an integrated EMR system that meets the needs of the population. Sure, there are big differences between the human and the pet market, as well as a lot of regulation driven largely by politics and expensive regulatory oversight. Maybe we should take a look at what’s going on in the veterinary information management industry and adopt a set of known best practices that seems to be working just fine for man’s best friends.

About the Author

Vicky Mahn-DiNicola is vice president of research and market insights at Midas+ Xerox, where she serves as a speaker, author, and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting, and healthcare transformation. A certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post-graduate studies at the University of Arizona, where she continues to serve as adjunct faculty. 

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While nearly everyone agrees that some readmissions are clinically necessary, the fact is that unplanned 30-day hospital readmissions for high-risk, complex clinical populations afflicted by conditions such as acute MI, heart failure, and pneumonia are not good for patients, nor good for our collective fiscal bottom line. Since 2012, the Centers for Medicare & Medicaid Services (CMS) has sanctioned hospitals economically for what they deem as excess 30-day readmissions (meaning more than expected, given a hospital’s risk-adjusted population), resulting in a significant reduction of Medicare revenue for approximately two-thirds of U.S. hospitals.  

Despite the fact that CMS has reported modest reductions in 30-day readmissions as a result of the measurement mandates from the Hospital Readmission Reduction Program, not everyone agrees on the manner in which “excess” readmissions are calculated for a given hospital. Sharp criticism points to the lack of risk adjustment for sociodemographic status (SDS) variables such as income, education, health literacy, culture, and other local and regional demographics, any of which may impact a successful care transition from hospital to home (and which hospitals have little to no direct control over). More research is needed to understand the impact of these variables, as well as the interventions that may reduce unnecessary 30-day readmissions. 

In response, the National Quality Forum (NQF) has initiated a two-year pilot program to examine the impact of risk-adjusting readmission metrics using socioeconomic and demographic variables. The pilot will not only examine the feasibility of securing the necessary data needed to risk-adjust for SDS variables, but also the impacts that such risk-adjustment might have on interpretation of the results. For example, safety-net hospitals, which provide care to larger numbers of Medicaid and dual-eligible patients, may have an increase in allowable 30-day readmissions as a result of SDS risk adjustment. The concern expressed by CMS and others is that this may lower the quality bar for some hospitals that provide care to the under-advantaged, thereby reducing the incentive for some hospitals to improve their strategies for care transitions.  

However, before this vital research can be completed, CMS is proposing yet another form of measurement related to post-discharge care transition. The proposed CMS Inpatient Prospective Payment System (IPPS) Rule for the 2016 fiscal year includes two new measures for acute-care hospitals. If passed in the final rule, scheduled for posting in August 2015, these measures will compute “excess days” for patients discharged following acute MI or heart failure. They not only count the number of days associated with a 30-day unplanned inpatient readmission event, but also include observation stay admissions and emergency department (ED) visits that occur within 30 days of discharge from an inpatient stay. ED visits (treat and release) will be counted as a half-day length of stay, and observation status readmissions will be rounded up to the nearest half day. 

While the proposed rule does not yet include financial sanctions for hospitals that exceed their number of “expected days,” there is little doubt that if it passes, these measures will evolve to either replace or expand the current 30-day unplanned readmission measures in the Hospital Readmission Reduction Program. Critics of these proposed measures suggest that they could impose a “double jeopardy” to hospitals that face financial penalties for excess readmissions, and now excess days, especially in the absence of risk-adjusted results that reflect the SDS status of their populations. 

This shift in measurement poses an enormous challenge to hospital care management teams. Most hospitals in the U.S. are already vigilant about decisions to admit to inpatient status and are utilizing the option to admit to lower-cost “observation status” for any hospitalization. However, these measures have the potential to further compress the providers’ options for patients returning to the hospital. Will hospitals feel pressured to “meet their numbers” by treating and releasing greater numbers of patients who come through the ED, rather than admitting them for closer observation?

If history is our teacher, we can look back on the effect of the two-midnight rule and the expansion of the Hospital Readmission Reduction Program for observation status admissions and ED visits. In the September 2014 Medicare Hospital Quality Chartbook, CMS concluded that “hospitals may be avoiding readmissions by placing more patients under observation stay status or keeping them in the ED.” 

The more optimistic perspective may be that this new measurement standard will contribute added incentive for hospitals and healthcare systems to implement more robust population health management (PHM) strategies to improve care transitions across their communities.

PHM interventions may consist of provider-centric and community-based strategies. Provider-centric interventions typically consist of aggressive care coordination, including post-discharge follow-up calls to patients, hospital and community case management services, disease management programs, medication reconciliation processes, and incentives to utilize provider-owned or managed post-acute care services. In addition, medical providers are incentivized to adopt standardized practice management protocols and pathways that show promise in reducing costs of services.   

An emerging trend in provider-based PHM strategies is the leveraging of community-based resources, not only for promotion of wellness and prevention, but also for care of the frail, elderly, and those with complex chronic diseases. Programs such as those of the American Heart, Lung, and Diabetes associations may be used to educate and support patients with chronic disease. Community and neighborhood health coalitions and faith-based organizations are leveraged to conduct wellness clinics and health promotion activities, as well as to provide care to homebound citizens in need of social support and assistance with activities of daily living.    

In addition, new partnerships are arising among hospital providers and other community assets, including partnerships with retail pharmacies, emergency medical services, schools, universities, and public and private transportation services, to name a few.

While hospitals can’t be expected to fix society, they can be a convening force within their communities to help drive understanding of the clinical, environmental and SDS determinants that impact health within the complexities of our healthcare system. The challenge will be to match the PHM interventions to the needs of the population, but there is no single strategy that will meet the needs of a diversely segmented population. A disease management program, for example, may be an effective intervention for a chronic disease population segment, but it will do little for a frail, elderly population or those with acute episodic needs.

Healthcare organizations require integrated health information systems and “big data” solutions in order to stratify the populations they are accountable for and strategically plan the rollout of interventions for their communities. They then will require longitudinal analytics to evaluate the effectiveness of their interventions. This may not be big news, but it certainly requires big strategy and leadership to execute. In the end, a balanced set of high-touch and high-tech innovations will be at the core of any tipping point we reach as the result of PHM interventions.   

So, how close are we to being able to implement effective PHM strategies in the U.S.? According to a recent poll of healthcare executives conducted by Midas+, a Xerox Company, only 15 percent of respondents reported that their organizations had a fully scaled PHM program in place today. An additional 30 percent reported that their programs were underway and would be ready within the next two years, and 48 percent reported that it would take another five to 10 years for their organizations to get there. Only a small minority of respondents reported that they wouldn’t get to the point of implementing PHM strategies or would likely become part of another healthcare system’s PHM strategy. 

The signal is clear that managing disease and promoting health within the traditional bricks and mortar of the hospital setting is only a small part of achieving greater gains in cost efficiency and quality of care. Mandated measurement and regulatory reporting by Medicare, as well as Medicaid and commercial payors, will continue to drive innovation in PHM strategies across the U.S.  

About the Author

Vicky Mahn-DiNicola is vice president of research and market insights at Midas+ Xerox, where she serves as a  speaker, author, and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting, and healthcare transformation. A certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post0graduate studies at the University of Arizona, where she continues to serve as an adjunct member of the faculty. 

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