HIM 360 Module 7 Project Two Example

Project Two: Complete, Compliant and Checked, a Documentation Integrity Plan for Juniper Health

[Student Name]

Southern New Hampshire University

HIM 360: Coding and Classifications Systems II

Project Two

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Project Two: Complete, Compliant and Checked, a Documentation Integrity Plan for Juniper Health

Project One showed that much of Juniper Health's high mortality ratio came from documentation rather than care, and the Module Six analysis showed a similar problem in our clinics, where chronic conditions vanish from the payment record each January. This plan responds to both. Its aim is a record that describes patients as sick as they are, no sicker and no healthier, with every change tested for compliance and every result checked by audit. It covers baseline gaps, goals, four interventions, measures, a timeline, resources and risks.

Baseline Gaps

Three gaps drive the plan. They are drawn from Juniper's own data and are summarized in Table 1. Each gap shows a difference between what clinicians know about a patient and what the coded record says, which is the definition of a documentation integrity problem.

Table 1. Baseline Documentation Gaps

Note. Figures come from the composite organization's internal reports.

Why the Gaps Exist

O'Malley et al. (2005) traced coding errors to each step between a patient's encounter and the final claim: what the patient reports, what the provider records, how legible and consistent the record is, and how the coder reads it. Juniper's gaps sit mostly at the provider step. Dietitians document malnutrition thoroughly, but coders may only code diagnoses documented by the treating provider, so a dietitian's note without a physician's agreement produces no code. In clinics, conditions are listed but not addressed because visits are short and focused on the complaint of the day. At the end of life, status decisions are recorded in orders and nursing notes but not in the physician documentation that coders use.

Goals

Every goal measures accuracy rather than volume. Within four quarters, Juniper will reach agreement between dietitian-identified and provider-documented malnutrition in at least 85% of qualifying patients; raise chronic condition recapture to 85% while keeping unsupported diagnoses found on audit below 3%; and document do-not-resuscitate and palliative care status in physician notes for at least 95% of inpatient deaths where orders show them. No goal is written in dollars or risk score points.

Intervention 1: A Malnutrition Pathway

Barker et al. (2011) described malnutrition as common among hospital patients, frequently unrecognized and linked to longer stays, more complications and higher costs. Recognition, not only coding, is the problem. The Academy of Nutrition and Dietetics and ASPEN consensus statement described by White et al. (2012) recommends diagnosing adult malnutrition from a set of observable characteristics, including energy intake, weight loss, loss of muscle or fat, fluid accumulation and grip strength, with at least two present.

Under the pathway, a dietitian who finds malnutrition by these criteria records the characteristics and severity in a structured note, and the record sends the treating provider a notification asking for review. The provider decides whether to agree, disagree or document another explanation. When the provider agrees, the progress note and discharge summary must name the diagnosis and its severity and state the treatment plan, so the diagnosis will survive a payer's clinical validation review. Coders never add malnutrition from the dietitian's note alone.

Intervention 2: Compliant Queries

When documentation is unclear or conflicting, a documentation specialist queries the provider. Juniper will rewrite its query templates to follow the joint AHIMA and ACDIS practice guidelines: queries present the clinical indicators from the record, offer every clinically reasonable option, include choices such as "other" and "unable to determine" and never mention payment or suggest which answer is preferred. Queries become part of the permanent business record. A monthly sample of 30 queries will be reviewed for leading language by a coder who did not write them.

Intervention 3: Pre-Visit Condition Review

Before each annual wellness visit and chronic care visit, a coder will review the prior year's record and send the clinician a short list of conditions that were documented before but not yet addressed this year, with the supporting evidence. The list is a prompt, not a diagnosis: the clinician must evaluate each condition at the visit and document its current status, or mark it resolved. Kronick and Welch (2014) showed how programs aimed at capturing diagnoses can raise risk scores faster than patients' health changes, so Juniper will audit a random 5% of visits touched by the review for unsupported diagnoses as well as missed ones.

Intervention 4: Education, Audit and Feedback

Hospitalists and primary care clinicians will receive short, case-based education on the documentation elements that coders need, including status decisions, present-on-admission conditions and condition specificity. Education alone rarely lasts, so each clinician will also receive a quarterly report comparing their documentation with their peers and with the goals. In a Cochrane review pooling 140 randomized trials, Ivers et al. (2012) reported that giving clinicians data on their own performance generally improved practice by a modest amount, and the gains were biggest for clinicians who started furthest behind. Feedback also worked better when it was given more than once, delivered by a supervisor or trusted peer, offered both verbally and on paper and tied to explicit targets with an action plan. Juniper's reports will be delivered by the physician advisor in department meetings and followed by a written summary with targets.

Measures

Table 2 lists the measures. Balancing measures watch for harm the plan could cause, such as leading queries or diagnoses added without support.

Table 2. Outcome, Process and Balancing Measures

Note. Targets set by the author with the documentation integrity steering group.

Timeline and Resources

The plan runs over four quarters. Quarter one rewrites query templates, builds the dietitian notification and trains documentation specialists. Quarter two launches the malnutrition pathway on two medical units and pre-visit review in three clinics. Quarter three extends both across the organization and delivers the first feedback reports. Quarter four audits results, compares them with the baseline and adjusts. Resources include one additional documentation specialist, a half-time auditor, dietitian time for structured notes, a physician advisor for four hours a week and information technology support for the notification and reports.

Risks and Compliance

The plan's main risk is that staff come to see it as a revenue program. Three safeguards address this. Goals and reports never show dollars; audits look for overcoding as hard as undercoding; and the compliance officer reviews query templates and audit results each quarter. A second risk is clinician fatigue from notifications. The pilot in quarter two will measure how many notifications each clinician receives and adjust thresholds before the program expands.

Limitations

Baseline figures come from twelve months of data and may shift with patient mix. Agreement between dietitians and providers measures consistency, not whether either is clinically right, so a sample of cases will receive an independent nutrition review. The plan also depends on physician time that competes with other priorities.

Conclusion

Juniper's documentation gaps come from how information moves between clinicians and coders, not from a lack of clinical knowledge. By routing dietitian findings to providers, writing fair queries, prompting clinicians to address known conditions and feeding results back to them, the plan aims for a record that is complete, compliant and checked. Success will mean a mortality ratio and risk scores that reflect patients as they are, whichever direction that moves the numbers.

References

Barker, L. A., Gout, B. S., & Crowe, T. C. (2011). Hospital malnutrition: Prevalence, identification and impact on patients and the healthcare system. International Journal of Environmental Research and Public Health, 8(2), 514-527. https://doi.org/10.3390/ijerph8020514

Ivers, N., Jamtvedt, G., Flottorp, S., Young, J. M., Odgaard-Jensen, J., French, S. D., O'Brien, M. A., Johansen, M., Grimshaw, J., & Oxman, A. D. (2012). Audit and feedback: Effects on professional practice and healthcare outcomes. Cochrane Database of Systematic Reviews, (6), Article CD000259. https://doi.org/10.1002/14651858.CD000259.pub3

Kronick, R., & Welch, W. P. (2014). Measuring coding intensity in the Medicare Advantage program. Medicare & Medicaid Research Review, 4(2), E1-E19. https://doi.org/10.5600/mmrr.004.02.sa06

O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring diagnoses: ICD code accuracy. Health Services Research, 40(5, Pt. 2), 1620-1639. https://doi.org/10.1111/j.1475-6773.2005.00444.x

White, J. V., Guenter, P., Jensen, G., Malone, A., & Schofield, M. (2012). Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: Characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). Journal of Parenteral and Enteral Nutrition, 36(3), 275-283. https://doi.org/10.1177/0148607112440285